ScaDaMaLe Course site and book

This notebook is from databricks document: - https://docs.databricks.com/_static/notebooks/package-cells.html

As you know, we need to eventually package and deploy our models, say using sbt (recall recommended home work 1).

However it is nice to be in a notebook environment to prototype and build intuition and create better pipelines.

Using package cells we can be in the best of both worlds to an extent.

Package Cells

Package cells are special cells that get compiled when executed. These cells have no visibility with respect to the rest of the notebook. You may think of them as separate scala files.

This means that only class and object definitions may go inside this cell. You may not have any variable or function definitions lying around by itself. The following cell will not work.

If you wish to use custom classes and/or objects defined within notebooks reliably in Spark, and across notebook sessions, you must use package cells to define those classes.

Unless you use package cells to define classes, you may also come across obscure bugs as follows:

// We define a class
case class TestKey(id: Long, str: String)
defined class TestKey
// we use that class as a key in the group by
val rdd = sc.parallelize(Array((TestKey(1L, "abd"), "dss"), (TestKey(2L, "ggs"), "dse"), (TestKey(1L, "abd"), "qrf")))

rdd.groupByKey().collect
rdd: org.apache.spark.rdd.RDD[(TestKey, String)] = ParallelCollectionRDD[0] at parallelize at command-2972105651607260:2
res0: Array[(TestKey, Iterable[String])] = Array((TestKey(2,ggs),CompactBuffer(dse)), (TestKey(1,abd),CompactBuffer(qrf)), (TestKey(1,abd),CompactBuffer(dss)))

What went wrong above? Even though we have two elements for the key TestKey(1L, "abd"), they behaved as two different keys resulting in:

Array[(TestKey, Iterable[String])] = Array(
  (TestKey(2,ggs),CompactBuffer(dse)), 
  (TestKey(1,abd),CompactBuffer(dss)), 
  (TestKey(1,abd),CompactBuffer(qrf)))

Once we define our case class within a package cell, we will not face this issue.

package com.databricks.example

case class TestKey(id: Long, str: String)
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import com.databricks.example

val rdd = sc.parallelize(Array(
  (example.TestKey(1L, "abd"), "dss"), (example.TestKey(2L, "ggs"), "dse"), (example.TestKey(1L, "abd"), "qrf")))

rdd.groupByKey().collect
import com.databricks.example
rdd: org.apache.spark.rdd.RDD[(com.databricks.example.TestKey, String)] = ParallelCollectionRDD[2] at parallelize at command-2972105651607263:3
res2: Array[(com.databricks.example.TestKey, Iterable[String])] = Array((TestKey(2,ggs),CompactBuffer(dse)), (TestKey(1,abd),CompactBuffer(dss, qrf)))

As you can see above, the group by worked above, grouping two elements (dss, qrf) under TestKey(1,abd).

These cells behave as individual source files, therefore only classes and objects can be defined inside these cells.

package x.y.z

val aNumber = 5 // won't work

def functionThatWillNotWork(a: Int): Int = a + 1

The following cell is the way to go.

package x.y.z

object Utils {
  val aNumber = 5 // works!
  def functionThatWillWork(a: Int): Int = a + 1
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.z.Utils

Utils.functionThatWillWork(Utils.aNumber)
import x.y.z.Utils
res5: Int = 6

Why did we get the warning: classes defined within packages cannot be redefined without a cluster restart?

Classes that get compiled with the package cells get dynamically injected into Spark's classloader. Currently it's not possible to remove classes from Spark's classloader. Any classes that you define and compile will have precedence in the classloader, therefore once you recompile, it will not be visible to your application.

Well that kind of beats the purpose of iterative notebook development if I have to restart the cluster, right? In that case, you may just rename the package to x.y.z2 during development/fast iteration and fix it once everything works.

One thing to remember with package cells is that it has no visiblity regarding the notebook environment.

  • The SparkContext will not be defined as sc.
  • The SQLContext will not be defined as sqlContext.
  • Did you import a package in a separate cell? Those imports will not be available in the package cell and have to be remade.
  • Variables imported through %run cells will not be available.

It is really a standalone file that just looks like a cell in a notebook. This means that any function that uses anything that was defined in a separate cell, needs to take that variable as a parameter or the class needs to take it inside the constructor.

package x.y.zpackage

import org.apache.spark.SparkContext

case class IntArray(values: Array[Int])

class MyClass(sc: SparkContext) {
  def sparkSum(array: IntArray): Int = {
    sc.parallelize(array.values).reduce(_ + _)
  }
}

object MyClass {
  def sparkSum(sc: SparkContext, array: IntArray): Int = {
    sc.parallelize(array.values).reduce(_ + _)
  }
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.zpackage._

val array = IntArray(Array(1, 2, 3, 4, 5))

val myClass = new MyClass(sc)
myClass.sparkSum(array)
import x.y.zpackage._
array: x.y.zpackage.IntArray = IntArray([I@44a91426)
myClass: x.y.zpackage.MyClass = x.y.zpackage.MyClass@5d3b142
res7: Int = 15
MyClass.sparkSum(sc, array)
res8: Int = 15

ScaDaMaLe Course site and book

Core ideas in Monte Carlo simulation

  • modular arithmetic gives pseudo-random streams that are indistiguishable from 'true' Uniformly distributed samples in integers from \({0,1,2,...,m}\)
  • by diving the integer streams from above by \(m\) we get samples from \({0/m,1/m,...,(m-1)/m}\) and "pretend" this to be samples from the Uniform(0,1) RV
  • we can use inverse distribution function of von Neumann's rejection sampler to convert samples from Uniform(0,1) RV to the following:
    • any other random variable
    • vector of random variables that could be dependent
    • or more generally other random structures:
      • random graphs and networks
      • random walks or (sensible perturbations of live traffic data on open street maps for hypothesis tests)
      • models of interacting paticle systems in ecology / chemcal physics, etc...

breeze.stats.distributions

Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.

import breeze.stats.distributions._

val poi = new Poisson(3.0);
import breeze.stats.distributions._
poi: breeze.stats.distributions.Poisson = Poisson(3.0)
val s = poi.sample(5); // let's draw five samples - black-box
s: IndexedSeq[Int] = Vector(2, 1, 1, 3, 2)

Getting probabilities of the Poisson samples

s.map( x => poi.probabilityOf(x) ) // PMF
res0: IndexedSeq[Double] = Vector(0.22404180765538775, 0.14936120510359185, 0.14936120510359185, 0.22404180765538775, 0.22404180765538775)
val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = MappedRand(Poisson(3.0),$Lambda$5455/238879880@20291ada)
breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
res1: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.9940000000000055,3.0370010010010025,1000)
(poi.mean, poi.variance) // population mean and variance
res2: (Double, Double) = (3.0,3.0)

Exponential random Variable

Let's focus on getting our hands direty with a common random variable.

NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.

val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
expo.rate // what is the rate parameter
res3: Double = 0.5

A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.

expo.probability(0, math.log(2) * expo.rate)
res4: Double = 0.5
expo.probability(math.log(2) * expo.rate, 10000.0)
res5: Double = 0.5
expo.probability(0.0, 1.5)
res6: Double = 0.950212931632136

The above result means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well.

1 - math.exp(-3.0) // the CDF of the Exponential RV with rate parameter 3
res7: Double = 0.950212931632136
val samples = expo.sample(2).sorted; // built-in black box - we will roll our own shortly in Spark
samples: IndexedSeq[Double] = Vector(1.0491479844289333, 2.5388976008276827)
expo.probability(samples(0), samples(1));
res8: Double = 0.1164316379209569
breeze.stats.meanAndVariance(expo.samples.take(10000)); // mean and variance of the sample
res9: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.0066329747737677,3.9408880555553485,10000)
(1 / expo.rate, 1 / (expo.rate * expo.rate)) // mean and variance of the population
res10: (Double, Double) = (2.0,4.0)
import spark.implicits._
import org.apache.spark.sql.functions._
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.range(1000).toDF("Id") // just make a DF of 1000 row indices
df: org.apache.spark.sql.DataFrame = [Id: bigint]
df.show(5)
+---+
| Id|
+---+
|  0|
|  1|
|  2|
|  3|
|  4|
+---+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id|                rand|
+---+--------------------+
|  0|0.024042816995877625|
|  1|  0.4763832311243641|
|  2|  0.3392911406256911|
|  3|  0.6282132043405342|
|  4|  0.9665960987271114|
+---+--------------------+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id|                rand|
+---+--------------------+
|  0|0.024042816995877625|
|  1|  0.4763832311243641|
|  2|  0.3392911406256911|
|  3|  0.6282132043405342|
|  4|  0.9665960987271114|
+---+--------------------+
only showing top 5 rows

dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
val dfRand = df.select($"Id", rand(seed=879664) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+-------------------+
| Id|               rand|
+---+-------------------+
|  0|  0.269224348263137|
|  1|0.20433965628039852|
|  2| 0.8481228617934538|
|  3| 0.6665103087537884|
|  4| 0.7712898780552342|
+---+-------------------+
only showing top 5 rows

dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]

Let's use the inverse CDF of the Exponential RV to transform these samples from the Uniform(0,1) RV into those from the Exponential RV.

val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
               .withColumn("one",lit(1.0))
               .withColumn("rate",lit(0.5))
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 2 more fields]
dfRand.show(5) 
+---+--------------------+---+----+
| Id|                rand|one|rate|
+---+--------------------+---+----+
|  0|0.024042816995877625|1.0| 0.5|
|  1|  0.4763832311243641|1.0| 0.5|
|  2|  0.3392911406256911|1.0| 0.5|
|  3|  0.6282132043405342|1.0| 0.5|
|  4|  0.9665960987271114|1.0| 0.5|
+---+--------------------+---+----+
only showing top 5 rows
val dfExpRand = dfRand.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand")) // samples from expo(rate=0.5)
dfExpRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
dfExpRand.show(5)
+---+--------------------+---+----+--------------------+
| Id|                rand|one|rate|         expo_sample|
+---+--------------------+---+----+--------------------+
|  0|0.024042816995877625|1.0| 0.5|0.048673126808362034|
|  1|  0.4763832311243641|1.0| 0.5|  1.2939904386814756|
|  2|  0.3392911406256911|1.0| 0.5|  0.8288839819270684|
|  3|  0.6282132043405342|1.0| 0.5|  1.9788694379168241|
|  4|  0.9665960987271114|1.0| 0.5|   6.798165162486205|
+---+--------------------+---+----+--------------------+
only showing top 5 rows
display(dfExpRand)
Id rand one rate expo_sample
0.0 2.4042816995877625e-2 1.0 0.5 4.8673126808362034e-2
1.0 0.4763832311243641 1.0 0.5 1.2939904386814756
2.0 0.3392911406256911 1.0 0.5 0.8288839819270684
3.0 0.6282132043405342 1.0 0.5 1.9788694379168241
4.0 0.9665960987271114 1.0 0.5 6.798165162486205
5.0 0.8269758822163711 1.0 0.5 3.508648570093974
6.0 0.3404052214826948 1.0 0.5 0.8322592089262
7.0 0.5658253891225227 1.0 0.5 1.6686169931673005
8.0 0.11737981749647353 1.0 0.5 0.24972063061386013
9.0 0.7848668745585088 1.0 0.5 3.072996508744745
10.0 0.10665116151243736 1.0 0.5 0.2255562755600773
11.0 0.34971775733163646 1.0 0.5 0.8606975816973311
12.0 3.349566781262969e-2 1.0 0.5 6.813899599567436e-2
13.0 0.334063669089834 1.0 0.5 0.8131224244912618
14.0 0.4105052702588069 1.0 0.5 1.0569789985263547
15.0 0.22519777483698333 1.0 0.5 0.5102949510683874
16.0 0.21124107883891907 1.0 0.5 0.47458910937069004
17.0 0.12332274767843698 1.0 0.5 0.26323273531964136
18.0 4.324422155449681e-2 1.0 0.5 8.841423006745963e-2
19.0 3.933267172708754e-2 1.0 0.5 8.025420479220831e-2
20.0 0.8162723223794007 1.0 0.5 3.388601261211285
21.0 7.785566240785313e-2 1.0 0.5 0.16210703862675058
22.0 0.1244015150504949 1.0 0.5 0.26569528726942954
23.0 0.5313795676534989 1.0 0.5 1.5159243018004147
24.0 0.8177063965639969 1.0 0.5 3.4042733720631824
25.0 0.7757379079578416 1.0 0.5 2.98987971469377
26.0 0.9019086568714294 1.0 0.5 4.643712323362237
27.0 0.5793202308042869 1.0 0.5 1.7317667559523853
28.0 4.913530139386124e-2 1.0 0.5 0.10076699863506142
29.0 0.6984668419742928 1.0 0.5 2.3977505839877837
30.0 1.579141234703818e-2 1.0 0.5 3.18348501444197e-2
31.0 0.3170147319677016 1.0 0.5 0.762563978285606
32.0 0.6243818511105477 1.0 0.5 1.9583644261768265
33.0 0.9720896733059052 1.0 0.5 7.157517052464175
34.0 6.406755887221727e-2 1.0 0.5 0.13242396678361196
35.0 0.8275324400058236 1.0 0.5 3.515092236401242
36.0 0.4223760255739688 1.0 0.5 1.0976643705892708
37.0 0.6237792697270514 1.0 0.5 1.9551585184791915
38.0 0.30676209726152626 1.0 0.5 0.7327640894184466
39.0 0.7209798363387625 1.0 0.5 2.5529424571699533
40.0 0.5408086036735253 1.0 0.5 1.556576340750279
41.0 0.22254408000797787 1.0 0.5 0.5034566621599345
42.0 0.6478051949133747 1.0 0.5 2.0871416658496442
43.0 0.6186563924196967 1.0 0.5 1.9281089062362353
44.0 0.2689179760895458 1.0 0.5 0.6264592354306907
45.0 0.1310898372764776 1.0 0.5 0.28103107825164747
46.0 0.8459785027719904 1.0 0.5 3.741326187841892
47.0 0.7844461581951896 1.0 0.5 3.069089109365284
48.0 0.3195541476806115 1.0 0.5 0.7700140609818293
49.0 0.326892158146161 1.0 0.5 0.7916994433570004
50.0 0.4392312327086285 1.0 0.5 1.1568932758900772
51.0 0.5377790694946909 1.0 0.5 1.543424595293625
52.0 0.4369640582174875 1.0 0.5 1.1488236262486446
53.0 0.26672242876690055 1.0 0.5 0.6204619408451196
54.0 0.9743181848790359 1.0 0.5 7.323944240755433
55.0 0.5636877255447679 1.0 0.5 1.6587941323713102
56.0 0.8214194684040824 1.0 0.5 3.44543124420649
57.0 4.2394218638494574e-2 1.0 0.5 8.663817482333575e-2
58.0 0.2986030735782996 1.0 0.5 0.7093626466953578
59.0 0.9251464009715654 1.0 0.5 5.184442172120483
60.0 0.9768531076059933 1.0 0.5 7.531789490600966
61.0 0.4147651110232632 1.0 0.5 1.0714839854387161
62.0 0.4525161010109643 1.0 0.5 1.2048444519262322
63.0 7.638776464132979e-2 1.0 0.5 0.1589259082490955
64.0 0.8203900440907219 1.0 0.5 3.433935381714252
65.0 0.9746746584977457 1.0 0.5 7.35189948830076
66.0 0.41832172801299305 1.0 0.5 1.083675562745256
67.0 3.3131200470495226e-2 1.0 0.5 6.738494114620364e-2
68.0 0.10008247055930286 1.0 0.5 0.21090430762250917
69.0 0.4268021831375646 1.0 0.5 1.113048783438383
70.0 0.8213380254753332 1.0 0.5 3.444519337826204
71.0 0.47431121070064797 1.0 0.5 1.2860917933318652
72.0 0.68679164992865 1.0 0.5 2.321773309424188
73.0 0.7057803676609208 1.0 0.5 2.4468574835462356
74.0 0.32184620342731496 1.0 0.5 0.776762356325894
75.0 0.3589329148022541 1.0 0.5 0.8892423408857425
76.0 0.7448782338623747 1.0 0.5 2.7320286670643386
77.0 0.9740613004640858 1.0 0.5 7.304038469785621
78.0 0.7453909609063684 1.0 0.5 2.736052180743762
79.0 6.853610233988816e-2 1.0 0.5 0.14199569380051374
80.0 0.336012781812836 1.0 0.5 0.8189847588182159
81.0 0.2963058480260412 1.0 0.5 0.7028229208813994
82.0 0.38627232525200883 1.0 0.5 0.9764079513820425
83.0 0.9097248909817963 1.0 0.5 4.809787008391862
84.0 0.20679745942261907 1.0 0.5 0.46335335878970363
85.0 0.16582497569469312 1.0 0.5 0.36262407472768277
86.0 0.6960738838737711 1.0 0.5 2.381941292346302
87.0 0.890094879396298 1.0 0.5 4.416275650715409
88.0 0.8258738496535992 1.0 0.5 3.4959504809275685
89.0 0.4066244237870885 1.0 0.5 1.0438554620679272
90.0 0.44414816275414526 1.0 0.5 1.1745070000344822
91.0 0.8820223175378497 1.0 0.5 4.274519608161631
92.0 0.3814200182827251 1.0 0.5 0.9606575597598919
93.0 0.491252177347836 1.0 0.5 1.3516056440673538
94.0 0.9042145561359308 1.0 0.5 4.691289097027222
95.0 0.8293982814391448 1.0 0.5 3.536847140695551
96.0 0.6957817964411346 1.0 0.5 2.38002012037681
97.0 0.46108317902841456 1.0 0.5 1.2363880819986401
98.0 0.658234439306857 1.0 0.5 2.14726054405596
99.0 0.43175037735005384 1.0 0.5 1.130388960612226
100.0 0.7719881477151886 1.0 0.5 2.9567153353469786
101.0 6.397418621476536e-2 1.0 0.5 0.13222444810891013
102.0 0.44410081132020296 1.0 0.5 1.1743366329905425
103.0 0.9364231799229901 1.0 0.5 5.511012678534471
104.0 0.5453471566845608 1.0 0.5 1.576442265948382
105.0 0.7346238968987211 1.0 0.5 2.653214404406468
106.0 0.3493365535175731 1.0 0.5 0.8595254994862054
107.0 0.5230037795263933 1.0 0.5 1.480493423321206
108.0 0.6886814284788837 1.0 0.5 2.333877090719847
109.0 0.7731252946239237 1.0 0.5 2.966714745163421
110.0 2.276168830750991e-2 1.0 0.5 4.604946957472576e-2
111.0 0.264951110753621 1.0 0.5 0.6156365319998442
112.0 0.14616140102686104 1.0 0.5 0.3160261944637388
113.0 3.066619523108849e-2 1.0 0.5 6.229248529326732e-2
114.0 0.49017052188807597 1.0 0.5 1.3473579316333943
115.0 9.835381148010536e-2 1.0 0.5 0.20706617613153588
116.0 8.828156810764243e-2 1.0 0.5 0.1848481470740138
117.0 0.6868446103815881 1.0 0.5 2.3221115183574854
118.0 0.4067862578001439 1.0 0.5 1.0444010055396156
119.0 0.44070042328022496 1.0 0.5 1.1621400679168603
120.0 0.19523663294276805 1.0 0.5 0.4344139974853618
121.0 0.34930320794183467 1.0 0.5 0.8594230049585315
122.0 0.37713232784782635 1.0 0.5 0.9468423740115679
123.0 0.6429635193876022 1.0 0.5 2.0598346316676848
124.0 0.5726782020473655 1.0 0.5 1.7004358488091253
125.0 0.9583607379261688 1.0 0.5 6.357423513924509
126.0 0.1736332737734787 1.0 0.5 0.38143325101594394
127.0 0.7191057722209122 1.0 0.5 2.539554188214649
128.0 0.5866772207662699 1.0 0.5 1.7670528869782172
129.0 0.44342504955089734 1.0 0.5 1.171906870972165
130.0 0.8638825079861409 1.0 0.5 3.988473708673089
131.0 0.19206484030065873 1.0 0.5 0.42654694315586256
132.0 6.329398370441452e-2 1.0 0.5 0.1307715918497558
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966.0 0.5508204944886083 1.0 0.5 1.6006653631535386
967.0 0.38693668974398643 1.0 0.5 0.9785741380105565
968.0 0.4548891238031203 1.0 0.5 1.2135321248948858
969.0 0.5765426183052531 1.0 0.5 1.7186048069928659
970.0 0.6043932471419058 1.0 0.5 1.8546692189299532
971.0 0.5540865320847946 1.0 0.5 1.615260727826734
972.0 0.6922149458884131 1.0 0.5 2.356707231797275
973.0 0.958785269414793 1.0 0.5 6.377919096104818
974.0 0.24053086467843443 1.0 0.5 0.5502711918533648
975.0 0.7280165406338818 1.0 0.5 2.6040280513783802
976.0 0.25983069480411236 1.0 0.5 0.601752656294586
977.0 0.9631430150183512 1.0 0.5 6.601420253137288
978.0 0.9618464899092873 1.0 0.5 6.532275035727719
979.0 0.1737093794671214 1.0 0.5 0.38161745300759264
980.0 0.6297685595655134 1.0 0.5 1.9872539084176686
981.0 0.6821539282076621 1.0 0.5 2.292376128719879
982.0 0.20134207234771373 1.0 0.5 0.4496451009588417
983.0 0.706334629023196 1.0 0.5 2.45062870766914
984.0 0.35171371567805576 1.0 0.5 0.8668457668004146
985.0 0.4418132575532645 1.0 0.5 1.1661234174105077
986.0 0.32739555551180655 1.0 0.5 0.7931957435520275
987.0 0.634158494553163 1.0 0.5 2.011110169198309
988.0 4.308559782735366e-3 1.0 0.5 8.635736747611403e-3
989.0 0.4107240255611958 1.0 0.5 1.0577213152606424
990.0 0.6688896259565301 1.0 0.5 2.2106070057291913
991.0 0.35953267557398183 1.0 0.5 0.891114349350007
992.0 0.7399069275541614 1.0 0.5 2.693431482154778
993.0 0.12692177456699705 1.0 0.5 0.27146024369971955
994.0 1.1762786512343526e-2 1.0 0.5 2.3665030858473123e-2
995.0 0.9174681392193306 1.0 0.5 4.989141737826638
996.0 0.17908478481936818 1.0 0.5 0.39467089008875134
997.0 0.6401255388358325 1.0 0.5 2.0440000546740205
998.0 0.23394182884995784 1.0 0.5 0.5329943413516208
999.0 0.5255738075065223 1.0 0.5 1.4912984419798878
dfExpRand.describe().show() // look sensible
+-------+-----------------+--------------------+----+----+--------------------+
|summary|               Id|                rand| one|rate|         expo_sample|
+-------+-----------------+--------------------+----+----+--------------------+
|  count|             1000|                1000|1000|1000|                1000|
|   mean|            499.5|  0.5021780527729537| 1.0| 0.5|  1.9988036562138296|
| stddev|288.8194360957494| 0.28411433707906236| 0.0| 0.0|  2.0004677979809777|
|    min|                0|7.927735525826707E-4| 1.0| 0.5|0.001586175927435...|
|    max|              999|  0.9989566209643947| 1.0| 0.5|  13.730581519926131|
+-------+-----------------+--------------------+----+----+--------------------+
val expoSamplesDF = spark.range(1000000000).toDF("Id") // just make a DF of 100 row indices
               .select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
               .withColumn("one",lit(1.0))
               .withColumn("rate",lit(0.5))
               .withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand"))
expoSamplesDF: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
expoSamplesDF.describe().show()
+-------+-------------------+--------------------+----------+----------+--------------------+
|summary|                 Id|                rand|       one|      rate|         expo_sample|
+-------+-------------------+--------------------+----------+----------+--------------------+
|  count|         1000000000|          1000000000|1000000000|1000000000|          1000000000|
|   mean| 4.99999999147278E8| 0.49999187098618697|       1.0|       0.5|  1.9999737649593539|
| stddev|2.886751347391513E8|  0.2886780497633785|       0.0|       0.0|   2.000022741029218|
|    min|                  0|1.200616051022507...|       1.0|       0.5|2.401232103486493...|
|    max|          999999999|  0.9999999996809935|       1.0|       0.5|  43.731619350261035|
+-------+-------------------+--------------------+----------+----------+--------------------+

Approximating Pi with Monte Carlo simulaitons

Uisng RDDs directly, let's estimate Pi.

//Calculate pi with Monte Carlo estimation
import scala.math.random

//make a very large unique set of 1 -> n 
val partitions = 2 
val n = math.min(100000L * partitions, Int.MaxValue).toInt 
val xs = 1 until n 

//split up n into the number of partitions we can use 
val rdd = sc.parallelize(xs, partitions).setName("'N values rdd'")

//generate a random set of points within a 2x2 square
val sample = rdd.map { i =>
  val x = random * 2 - 1
  val y = random * 2 - 1
  (x, y)
}.setName("'Random points rdd'")

//points w/in the square also w/in the center circle of r=1
val inside = sample.filter { case (x, y) => (x * x + y * y < 1) }.setName("'Random points inside circle'")
val count = inside.count()
 
//Area(circle)/Area(square) = inside/n => pi=4*inside/n                        
println("Pi is roughly " + 4.0 * count / n)
Pi is roughly 3.1423
import scala.math.random
partitions: Int = 2
n: Int = 200000
xs: scala.collection.immutable.Range = Range 1 until 200000
rdd: org.apache.spark.rdd.RDD[Int] = 'N values rdd' ParallelCollectionRDD[38] at parallelize at command-2744653148555222:10
sample: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points rdd' MapPartitionsRDD[39] at map at command-2744653148555222:13
inside: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points inside circle' MapPartitionsRDD[40] at filter at command-2744653148555222:20
count: Long = 157115

Doing it in PySpark is just as easy. This may be needed if there are pyhton libraries you want to take advantage of in Spark.

# # Estimating $\pi$
#
# This PySpark example shows you how to estimate $\pi$ in parallel
# using Monte Carlo integration.

from __future__ import print_function
import sys
from random import random
from operator import add

partitions = 2
n = 100000 * partitions

def f(_):
    x = random() * 2 - 1
    y = random() * 2 - 1
    return 1 if x ** 2 + y ** 2 < 1 else 0

# To access the associated SparkContext
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
Pi is roughly 3.138820

The following is from this google turotial.

Programming a Monte Carlo simulation in Scala

Monte Carlo, of course, is famous as a gambling destination. In this section, you use Scala to create a simulation that models the mathematical advantage that a casino enjoys in a game of chance. The "house edge" at a real casino varies widely from game to game; it can be over 20% in keno, for example. This tutorial creates a simple game where the house has only a one-percent advantage. Here's how the game works:

  1. The player places a bet, consisting of a number of chips from a bankroll fund.
  • The player rolls a 100-sided die (how cool would that be?).
  • If the result of the roll is a number from 1 to 49, the player wins.
  • For results 50 to 100, the player loses the bet.

You can see that this game creates a one-percent disadvantage for the player: in 51 of the 100 possible outcomes for each roll, the player loses.

Follow these steps to create and run the game:

val STARTING_FUND = 10
val STAKE = 1   // the amount of the bet
val NUMBER_OF_GAMES = 25

def rollDie: Int = {
    val r = scala.util.Random
    r.nextInt(99) + 1
}

def playGame(stake: Int): (Int) = {
    val faceValue = rollDie
    if (faceValue < 50)
        (2*stake)
    else
        (0)
}

// Function to play the game multiple times
// Returns the final fund amount
def playSession(
   startingFund: Int = STARTING_FUND,
   stake: Int = STAKE,
   numberOfGames: Int = NUMBER_OF_GAMES):
   (Int) = {

    // Initialize values
    var (currentFund, currentStake, currentGame) = (startingFund, 0, 1)

    // Keep playing until number of games is reached or funds run out
    while (currentGame <= numberOfGames && currentFund > 0) {

        // Set the current bet and deduct it from the fund
        currentStake = math.min(stake, currentFund)
        currentFund -= currentStake

        // Play the game
        val (winnings) = playGame(currentStake)

        // Add any winnings
        currentFund += winnings

        // Increment the loop counter
        currentGame += 1
    }
    (currentFund)
}
STARTING_FUND: Int = 10
STAKE: Int = 1
NUMBER_OF_GAMES: Int = 25
rollDie: Int
playGame: (stake: Int)Int
playSession: (startingFund: Int, stake: Int, numberOfGames: Int)Int

Enter the following code to play the game 25 times, which is the default value for NUMBER_OF_GAMES.

playSession()
res31: Int = 5

Your bankroll started with a value of 10 units. Is it higher or lower, now?

Now simulate 10,000 players betting 100 chips per game. Play 10,000 games in a session. This Monte Carlo simulation calculates the probability of losing all your money before the end of the session. Enter the follow code:

(sc.parallelize(1 to 10000, 500)
  .map(i => playSession(100000, 100, 250000))
  .map(i => if (i == 0) 1 else 0)
  .reduce(_+_)/10000.0)
res32: Double = 0.9992

Note that the syntax .reduce(_+_) is shorthand in Scala for aggregating by using a summing function.

The preceding code performs the following steps:

  1. Creates an RDD with the results of playing the session.
  • Replaces bankrupt players' results with the number 1 and nonzero results with the number 0.
  • Sums the count of bankrupt players.
  • Divides the count by the number of players.

A typical result might be:

res32: Double = 0.9992

Which represents a near guarantee of losing all your money, even though the casino had only a one-percent advantage.

Project Ideas

Try to create a scalable simulation of interest to you.

Here are some mature projects:

  • https://github.com/zishanfu/GeoSparkSim
  • https://github.com/srbaird/mc-var-spark

See a more complete VaR modeling: - https://databricks.com/blog/2020/05/27/modernizing-risk-management-part-1-streaming-data-ingestion-rapid-model-development-and-monte-carlo-simulations-at-scale.html

ScaDaMaLe Course site and book

Introduction to Machine Learning

Some very useful resources we will weave around for Statistical Learning, Data Mining, Machine Learning:

Deep Learning is a very popular method currently (2017) in Machine Learning and I recommend Andrew Ng's free course in Courseera for this.

Note: This is an applied course in data science and we will quickly move to doing things with data. You have to do work to get a deeper mathematical understanding or take other courses. We will focus on intution here and the distributed ML Pipeline in action.

I am assuming several of you have or are taking ML courses from experts in IT Department at Uppsala (if not, you might want to consider this seriously). In this course we will focus on getting our hads dirty quickly with some level of common understanding across all the disciplines represented here. Please dive deep at your own time to desired depths and tangents based on your background.

I may consider teaching a short theoretical course in statistical learning if there is enough interest for those with background in:

  • Real Analysis,
  • Geometry,
  • Combinatorics and
  • Probability, in the future.

Such a course could be an expanded version of the following notes built on the classic works of Luc Devroye and the L1-School of Statistical Learning:

Ameet's Summary of Machine Learning at a High Level

  • rough definition of machine learning.
    • constructing and studying methods that learn from and make predictions on data.
  • This broad area involves tools and ideas from various domains, including:
    • computer science,
    • probability and statistics,
    • optimization, and
    • linear algebra.
  • Common examples of ML, include:
    • face recognition,
    • link prediction,
    • text or document classification, eg.::
      • spam detection,
      • protein structure prediction
      • teaching computers to play games (go!)

Some common terminology

using example of spam detection as a running example.
  • the data points we learn from are call observations:

    • they are items or entities used for::
      • learning or
      • evaluation.
  • in the context of spam detection,

    • emails are our observations.
    • Features are attributes used to represent an observation.
    • Features are typically numeric,
      • and in spam detection, they can be:
      • the length,
      • the date, or
      • the presence or absence of keywords in emails.
    • Labels are values or categories assigned to observations.
      • and in spam detection, they can be:
      • an email being defined as spam or not spam.
  • Training and test data sets are the observations that we use to train and evaluate a learning algorithm. ...

  • Pop-Quiz

    • What is the difference between supervised and unsupervised learning?

Take your own notes if you want ....

ScaDaMaLe Course site and book

Million Song Dataset - Kaggle Challenge

Predict which songs a user will listen to.

SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.

When you first hear a song, do you ever categorize it as slow or fast in your head? Is it even a valid categorization? If so, can one do it automatically? I have always wondered about that. That is why I got excited when I learned about the Million Songs Dataset -Kaggle Challenge.

In this tutorial we will walk through a practical example of a data science project with Apache Spark in Python. We are going to parse, explore and model a sample from the million songs dataset stored on distributed storage. This tutorial is organized into three sections:

  1. ETL: Parses raw texts and creates a cached table
  2. Explore: Explores different aspects of the songs table using graphs
  3. Model: Uses SparkML to cluster songs based on some of their attributes

End to End Data Science

The goal of this tutorial is to prepare you for real world data science projects. Make sure you go through the tutorial in the above order and use the exercises to make yourself familiar further with the API. Also make sure you run these notebooks on a 1.6.x cluster.

1. ETL

The first step of most data science projects is extracting, transforming and loading data into well formated tables. Our example starts with ETL as well. By following the ETL noteboook you can expect to learn about following Spark concepts:

  • RDD: Resilient Distributed Dataset
  • Reading and transforming RDDs
  • Schema in Spark
  • Spark DataFrame
  • Temp tables
  • Caching tables

2. Explore

Exploratory analysis is a key step in any real data project. Data scientists use variety of tools to explore and visualize their data. In the second notebook of this tutorial we introduce several tools in Python and Databricks notebooks that can help you visually explore your large data. By reading this notebook and finishing its exercises you will become familiar with:

  • How to view the schema of a table
  • How to display ggplot and matplotlib figures in Notebooks
  • How to summarize and visualize different aspects of large datasets
  • How to sample and visualize large data

3. Model

The end goal of many data scientists is producing useful models. These models are often used for prediction of new and upcoming events in production. In our third notebook we construct a simple K-means clustering model. In this notebook you will learn about:

  • Feature transformation
  • Fitting a model using SparkML API
  • Applying a model to data
  • Visualizing model results
  • Model tuning

ScaDaMaLe Course site and book

Million Song Dataset - Kaggle Challenge

Predict which songs a user will listen to.

SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.

CAUTION: This notebook is expected to have an error in command 28 (Cmd 28 in databricks notebook). You are meant to learn how to fix this error with simple exception-handling to become a better data scientist. So ignore this warning, if any.

Stage 1: Parsing songs data

ETL

This is the first notebook in this tutorial. In this notebook we will read data from DBFS (DataBricks FileSystem). We will parse data and load it as a table that can be readily used in following notebooks.

By going through this notebook you can expect to learn how to read distributed data as an RDD, how to transform RDDs, and how to construct a Spark DataFrame from an RDD and register it as a table.

We first explore different files in our distributed file system. We use a header file to construct a Spark Schema object. We write a function that takes the header and casts strings in each line of our data to corresponding types. Once we run this function on the data we find that it fails on some corner caes. We update our function and finally get a parsed RDD. We combine that RDD and the Schema to construct a DataFame and register it as a temporary table in SparkSQL.

Text data files are stored in dbfs:/databricks-datasets/songs/data-001

You can conveniently list files on distributed file system (DBFS, S3 or HDFS) using %fs commands.

ls /databricks-datasets/songs/data-001/
path name size
dbfs:/databricks-datasets/songs/data-001/header.txt header.txt 377.0
dbfs:/databricks-datasets/songs/data-001/part-00000 part-00000 52837.0
dbfs:/databricks-datasets/songs/data-001/part-00001 part-00001 52469.0
dbfs:/databricks-datasets/songs/data-001/part-00002 part-00002 51778.0
dbfs:/databricks-datasets/songs/data-001/part-00003 part-00003 50551.0
dbfs:/databricks-datasets/songs/data-001/part-00004 part-00004 53449.0
dbfs:/databricks-datasets/songs/data-001/part-00005 part-00005 53301.0
dbfs:/databricks-datasets/songs/data-001/part-00006 part-00006 54184.0
dbfs:/databricks-datasets/songs/data-001/part-00007 part-00007 50924.0
dbfs:/databricks-datasets/songs/data-001/part-00008 part-00008 52533.0
dbfs:/databricks-datasets/songs/data-001/part-00009 part-00009 54570.0
dbfs:/databricks-datasets/songs/data-001/part-00010 part-00010 54338.0
dbfs:/databricks-datasets/songs/data-001/part-00011 part-00011 51836.0
dbfs:/databricks-datasets/songs/data-001/part-00012 part-00012 52297.0
dbfs:/databricks-datasets/songs/data-001/part-00013 part-00013 52044.0
dbfs:/databricks-datasets/songs/data-001/part-00014 part-00014 50704.0
dbfs:/databricks-datasets/songs/data-001/part-00015 part-00015 54158.0
dbfs:/databricks-datasets/songs/data-001/part-00016 part-00016 50080.0
dbfs:/databricks-datasets/songs/data-001/part-00017 part-00017 47708.0
dbfs:/databricks-datasets/songs/data-001/part-00018 part-00018 8858.0
dbfs:/databricks-datasets/songs/data-001/part-00019 part-00019 53323.0
dbfs:/databricks-datasets/songs/data-001/part-00020 part-00020 57877.0
dbfs:/databricks-datasets/songs/data-001/part-00021 part-00021 52491.0
dbfs:/databricks-datasets/songs/data-001/part-00022 part-00022 54791.0
dbfs:/databricks-datasets/songs/data-001/part-00023 part-00023 50682.0
dbfs:/databricks-datasets/songs/data-001/part-00024 part-00024 52863.0
dbfs:/databricks-datasets/songs/data-001/part-00025 part-00025 47416.0
dbfs:/databricks-datasets/songs/data-001/part-00026 part-00026 50130.0
dbfs:/databricks-datasets/songs/data-001/part-00027 part-00027 53462.0
dbfs:/databricks-datasets/songs/data-001/part-00028 part-00028 54179.0
dbfs:/databricks-datasets/songs/data-001/part-00029 part-00029 52738.0
dbfs:/databricks-datasets/songs/data-001/part-00030 part-00030 54159.0
dbfs:/databricks-datasets/songs/data-001/part-00031 part-00031 51247.0
dbfs:/databricks-datasets/songs/data-001/part-00032 part-00032 51610.0
dbfs:/databricks-datasets/songs/data-001/part-00033 part-00033 53895.0
dbfs:/databricks-datasets/songs/data-001/part-00034 part-00034 53125.0
dbfs:/databricks-datasets/songs/data-001/part-00035 part-00035 54066.0
dbfs:/databricks-datasets/songs/data-001/part-00036 part-00036 54265.0
dbfs:/databricks-datasets/songs/data-001/part-00037 part-00037 54264.0
dbfs:/databricks-datasets/songs/data-001/part-00038 part-00038 50540.0
dbfs:/databricks-datasets/songs/data-001/part-00039 part-00039 55193.0
dbfs:/databricks-datasets/songs/data-001/part-00040 part-00040 54537.0
dbfs:/databricks-datasets/songs/data-001/part-00041 part-00041 52402.0
dbfs:/databricks-datasets/songs/data-001/part-00042 part-00042 54673.0
dbfs:/databricks-datasets/songs/data-001/part-00043 part-00043 53009.0
dbfs:/databricks-datasets/songs/data-001/part-00044 part-00044 51789.0
dbfs:/databricks-datasets/songs/data-001/part-00045 part-00045 52986.0
dbfs:/databricks-datasets/songs/data-001/part-00046 part-00046 54442.0
dbfs:/databricks-datasets/songs/data-001/part-00047 part-00047 52971.0
dbfs:/databricks-datasets/songs/data-001/part-00048 part-00048 53331.0
dbfs:/databricks-datasets/songs/data-001/part-00049 part-00049 44263.0
dbfs:/databricks-datasets/songs/data-001/part-00050 part-00050 54841.0
dbfs:/databricks-datasets/songs/data-001/part-00051 part-00051 54306.0
dbfs:/databricks-datasets/songs/data-001/part-00052 part-00052 53610.0
dbfs:/databricks-datasets/songs/data-001/part-00053 part-00053 53573.0
dbfs:/databricks-datasets/songs/data-001/part-00054 part-00054 53854.0
dbfs:/databricks-datasets/songs/data-001/part-00055 part-00055 54236.0
dbfs:/databricks-datasets/songs/data-001/part-00056 part-00056 54455.0
dbfs:/databricks-datasets/songs/data-001/part-00057 part-00057 52307.0
dbfs:/databricks-datasets/songs/data-001/part-00058 part-00058 52313.0
dbfs:/databricks-datasets/songs/data-001/part-00059 part-00059 52446.0
dbfs:/databricks-datasets/songs/data-001/part-00060 part-00060 51958.0
dbfs:/databricks-datasets/songs/data-001/part-00061 part-00061 53859.0
dbfs:/databricks-datasets/songs/data-001/part-00062 part-00062 53698.0
dbfs:/databricks-datasets/songs/data-001/part-00063 part-00063 54482.0
dbfs:/databricks-datasets/songs/data-001/part-00064 part-00064 40182.0
dbfs:/databricks-datasets/songs/data-001/part-00065 part-00065 54410.0
dbfs:/databricks-datasets/songs/data-001/part-00066 part-00066 49123.0
dbfs:/databricks-datasets/songs/data-001/part-00067 part-00067 50796.0
dbfs:/databricks-datasets/songs/data-001/part-00068 part-00068 49561.0
dbfs:/databricks-datasets/songs/data-001/part-00069 part-00069 52294.0
dbfs:/databricks-datasets/songs/data-001/part-00070 part-00070 51250.0
dbfs:/databricks-datasets/songs/data-001/part-00071 part-00071 58942.0
dbfs:/databricks-datasets/songs/data-001/part-00072 part-00072 54589.0
dbfs:/databricks-datasets/songs/data-001/part-00073 part-00073 54233.0
dbfs:/databricks-datasets/songs/data-001/part-00074 part-00074 54725.0
dbfs:/databricks-datasets/songs/data-001/part-00075 part-00075 54877.0
dbfs:/databricks-datasets/songs/data-001/part-00076 part-00076 54333.0
dbfs:/databricks-datasets/songs/data-001/part-00077 part-00077 51927.0
dbfs:/databricks-datasets/songs/data-001/part-00078 part-00078 51744.0
dbfs:/databricks-datasets/songs/data-001/part-00079 part-00079 53187.0
dbfs:/databricks-datasets/songs/data-001/part-00080 part-00080 43246.0
dbfs:/databricks-datasets/songs/data-001/part-00081 part-00081 54269.0
dbfs:/databricks-datasets/songs/data-001/part-00082 part-00082 48464.0
dbfs:/databricks-datasets/songs/data-001/part-00083 part-00083 52144.0
dbfs:/databricks-datasets/songs/data-001/part-00084 part-00084 53375.0
dbfs:/databricks-datasets/songs/data-001/part-00085 part-00085 55139.0
dbfs:/databricks-datasets/songs/data-001/part-00086 part-00086 50924.0
dbfs:/databricks-datasets/songs/data-001/part-00087 part-00087 52013.0
dbfs:/databricks-datasets/songs/data-001/part-00088 part-00088 54262.0
dbfs:/databricks-datasets/songs/data-001/part-00089 part-00089 53007.0
dbfs:/databricks-datasets/songs/data-001/part-00090 part-00090 55142.0
dbfs:/databricks-datasets/songs/data-001/part-00091 part-00091 52049.0
dbfs:/databricks-datasets/songs/data-001/part-00092 part-00092 54714.0
dbfs:/databricks-datasets/songs/data-001/part-00093 part-00093 52906.0
dbfs:/databricks-datasets/songs/data-001/part-00094 part-00094 52188.0
dbfs:/databricks-datasets/songs/data-001/part-00095 part-00095 50768.0
dbfs:/databricks-datasets/songs/data-001/part-00096 part-00096 55242.0
dbfs:/databricks-datasets/songs/data-001/part-00097 part-00097 52059.0
dbfs:/databricks-datasets/songs/data-001/part-00098 part-00098 52982.0
dbfs:/databricks-datasets/songs/data-001/part-00099 part-00099 52015.0
dbfs:/databricks-datasets/songs/data-001/part-00100 part-00100 51467.0
dbfs:/databricks-datasets/songs/data-001/part-00101 part-00101 50926.0
dbfs:/databricks-datasets/songs/data-001/part-00102 part-00102 55018.0
dbfs:/databricks-datasets/songs/data-001/part-00103 part-00103 50043.0
dbfs:/databricks-datasets/songs/data-001/part-00104 part-00104 51936.0
dbfs:/databricks-datasets/songs/data-001/part-00105 part-00105 57311.0
dbfs:/databricks-datasets/songs/data-001/part-00106 part-00106 55090.0
dbfs:/databricks-datasets/songs/data-001/part-00107 part-00107 54396.0
dbfs:/databricks-datasets/songs/data-001/part-00108 part-00108 56594.0
dbfs:/databricks-datasets/songs/data-001/part-00109 part-00109 53260.0
dbfs:/databricks-datasets/songs/data-001/part-00110 part-00110 42007.0
dbfs:/databricks-datasets/songs/data-001/part-00119 part-00119 0.0

As you can see in the listing we have data files and a single header file. The header file seems interesting and worth a first inspection at first. The file is 377 bytes, therefore it is safe to collect the entire content of the file in the notebook.

sc.textFile("databricks-datasets/songs/data-001/header.txt").collect() 
res1: Array[String] = Array(artist_id:string, artist_latitude:double, artist_longitude:double, artist_location:string, artist_name:string, duration:double, end_of_fade_in:double, key:int, key_confidence:double, loudness:double, release:string, song_hotnes:double, song_id:string, start_of_fade_out:double, tempo:double, time_signature:double, time_signature_confidence:double, title:string, year:double, partial_sequence:int)

Remember you can collect() a huge RDD and crash the driver program - so it is a good practise to take a couple lines and count the number of lines, especially if you have no idea what file you are trying to read.

sc.textFile("databricks-datasets/songs/data-001/header.txt").take(2)
res2: Array[String] = Array(artist_id:string, artist_latitude:double)
sc.textFile("databricks-datasets/songs/data-001/header.txt").count()
res3: Long = 20
sc.textFile("databricks-datasets/songs/data-001/header.txt").collect.map(println) // uncomment to see line-by-line
artist_id:string
artist_latitude:double
artist_longitude:double
artist_location:string
artist_name:string
duration:double
end_of_fade_in:double
key:int
key_confidence:double
loudness:double
release:string
song_hotnes:double
song_id:string
start_of_fade_out:double
tempo:double
time_signature:double
time_signature_confidence:double
title:string
year:double
partial_sequence:int
res4: Array[Unit] = Array((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())

As seen above each line in the header consists of a name and a type separated by colon. We will need to parse the header file as follows:

val header = sc.textFile("/databricks-datasets/songs/data-001/header.txt").map(line => {
                val headerElement = line.split(":")
                (headerElement(0), headerElement(1))
            }
           ).collect()
header: Array[(String, String)] = Array((artist_id,string), (artist_latitude,double), (artist_longitude,double), (artist_location,string), (artist_name,string), (duration,double), (end_of_fade_in,double), (key,int), (key_confidence,double), (loudness,double), (release,string), (song_hotnes,double), (song_id,string), (start_of_fade_out,double), (tempo,double), (time_signature,double), (time_signature_confidence,double), (title,string), (year,double), (partial_sequence,int))

Let's define a case class called Song that will be used to represent each row of data in the files:

  • /databricks-datasets/songs/data-001/part-00000 through /databricks-datasets/songs/data-001/part-00119 or the last .../part-***** file.
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
defined class Song

Now we turn to data files. First, step is inspecting the first line of data to inspect its format.

// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*") 
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3258] at textFile at command-2972105651607016:2
dataRDD.count // number of songs
res5: Long = 31369
dataRDD.take(3)
res6: Array[String] = Array(AR81V6H1187FB48872	nan	nan		Earl Sixteen	213.7073	0.0	11	0.419	-12.106	Soldier of Jah Army	nan	SOVNZSZ12AB018A9B8	208.289	125.882	1	0.0	Rastaman	2003	--, ARVVZQP11E2835DBCB	nan	nan		Wavves	133.25016	0.0	0	0.282	0.596	Wavvves	0.471578247701	SOJTQHQ12A8C143C5F	128.116	89.519	1	0.0	I Want To See You (And Go To The Movies)	2009	--, ARFG9M11187FB3BBCB	nan	nan	Nashua USA	C-Side	247.32689	0.0	9	0.612	-4.896	Santa Festival Compilation 2008 vol.1	nan	SOAJSQL12AB0180501	242.196	171.278	5	1.0	Loose on the Dancefloor	0	225261)

Each line of data consists of multiple fields separated by \t. With that information and what we learned from the header file, we set out to parse our data.

  • We have already created a case class based on the header (which seems to agree with the 3 lines above).
  • Next, we will create a function that takes each line as input and returns the case class as output.
// let's do this 'by hand' to re-flex our RDD-muscles :)
// although this is not a robust way to read from a data engineering perspective (without fielding exceptions)
def parseLine(line: String): Song = {
  
  val tokens = line.split("\t")
  Song(tokens(0), tokens(1).toDouble, tokens(2).toDouble, tokens(3), tokens(4), tokens(5).toDouble, tokens(6).toDouble, tokens(7).toInt, tokens(8).toDouble, tokens(9).toDouble, tokens(10), tokens(11).toDouble, tokens(12), tokens(13).toDouble, tokens(14).toDouble, tokens(15).toDouble, tokens(16).toDouble, tokens(17), tokens(18).toDouble, tokens(19).toInt)
}
parseLine: (line: String)Song

With this function we can transform the dataRDD to another RDD that consists of Song case classes

val parsedRDD = dataRDD.map(parseLine)
parsedRDD: org.apache.spark.rdd.RDD[Song] = MapPartitionsRDD[3259] at map at command-2972105651607022:1

To convert an RDD of case classes to a DataFrame, we just need to call the toDF method

val df = parsedRDD.toDF
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]

Once we get a DataFrame we can register it as a temporary table. That will allow us to use its name in SQL queries.

df.createOrReplaceTempView("songsTable")

We can now cache our table. So far all operations have been lazy. This is the first time Spark will attempt to actually read all our data and apply the transformations.

If you are running Spark 1.6+ the next command will throw a parsing error.

cache table songsTable

The error means that we are trying to convert a missing value to a Double. Here is an updated version of the parseLine function to deal with missing values

// good data engineering science practise
def parseLine(line: String): Song = {
  
  
  def toDouble(value: String, defaultVal: Double): Double = {
    try {
       value.toDouble
    } catch {
      case e: Exception => defaultVal
    }
  }

  def toInt(value: String, defaultVal: Int): Int = {
    try {
       value.toInt
      } catch {
      case e: Exception => defaultVal
    }
  }
  
  val tokens = line.split("\t")
  Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
parseLine: (line: String)Song
val df = dataRDD.map(parseLine).toDF
df.createOrReplaceTempView("songsTable")
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]

And let's try caching the table. We are going to access this data multiple times in following notebooks, therefore it is a good idea to cache it in memory for faster subsequent access.

cache table songsTable

From now on we can easily query our data using the temporary table we just created and cached in memory. Since it is registered as a table we can conveniently use SQL as well as Spark API to access it.

select * from songsTable limit 10
artist_id artist_latitude artist_longitude artist_location artist_name duration end_of_fade_in key key_confidence loudness release song_hotness song_id start_of_fade_out tempo time_signature time_signature_confidence title year partial_sequence
AR81V6H1187FB48872 0.0 0.0 Earl Sixteen 213.7073 0.0 11.0 0.419 -12.106 Soldier of Jah Army 0.0 SOVNZSZ12AB018A9B8 208.289 125.882 1.0 0.0 Rastaman 2003.0 -1.0
ARVVZQP11E2835DBCB 0.0 0.0 Wavves 133.25016 0.0 0.0 0.282 0.596 Wavvves 0.471578247701 SOJTQHQ12A8C143C5F 128.116 89.519 1.0 0.0 I Want To See You (And Go To The Movies) 2009.0 -1.0
ARFG9M11187FB3BBCB 0.0 0.0 Nashua USA C-Side 247.32689 0.0 9.0 0.612 -4.896 Santa Festival Compilation 2008 vol.1 0.0 SOAJSQL12AB0180501 242.196 171.278 5.0 1.0 Loose on the Dancefloor 0.0 225261.0
ARK4Z2O1187FB45FF0 0.0 0.0 Harvest 337.05751 0.247 4.0 0.46 -9.092 Underground Community 0.0 SOTDRVW12AB018BEB9 327.436 84.986 4.0 0.673 No Return 0.0 101619.0
AR4VQSG1187FB57E18 35.25082 -91.74015 Searcy, AR Gossip 430.23628 0.0 2.0 3.4e-2 -6.846 Yr Mangled Heart 0.0 SOTVOCL12A8AE478DD 424.06 121.998 4.0 0.847 Yr Mangled Heart 2006.0 740623.0
ARNBV1X1187B996249 0.0 0.0 Alex 186.80118 0.0 4.0 0.641 -16.108 Jolgaledin 0.0 SODTGRY12AB0182438 166.156 140.735 4.0 5.5e-2 Mariu Sonur Jesus 0.0 673970.0
ARXOEZX1187B9B82A1 0.0 0.0 Elie Attieh 361.89995 0.0 7.0 0.863 -4.919 ELITE 0.0 SOIINTJ12AB0180BA6 354.476 128.024 4.0 0.399 Fe Yom We Leila 0.0 280304.0
ARXPUIA1187B9A32F1 0.0 0.0 Rome, Italy Simone Cristicchi 220.00281 2.119 4.0 0.486 -6.52 Dall'Altra Parte Del Cancello 0.484225272411 SONHXJK12AAF3B5290 214.761 99.954 1.0 0.928 L'Italiano 2007.0 745962.0
ARNPPTH1187B9AD429 51.4855 -0.37196 Heston, Middlesex, England Jimmy Page 156.86485 0.334 7.0 0.493 -9.962 No Introduction Necessary [Deluxe Edition] 0.0 SOGUHGW12A58A80E06 149.269 162.48 4.0 0.534 Wailing Sounds 2004.0 599250.0
AROGWRA122988FEE45 0.0 0.0 Christos Dantis 256.67873 2.537 9.0 0.742 -13.404 Daktilika Apotipomata 0.0 SOJJOYI12A8C13399D 248.912 134.944 4.0 0.162 Stin Proigoumeni Zoi 0.0 611396.0

Next up is exploring this data. Click on the Exploration notebook to continue the tutorial.

ScaDaMaLe Course site and book

Million Song Dataset - Kaggle Challenge

Predict which songs a user will listen to.

SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.

Stage 2: Exploring songs data

Explore

This is the second notebook in this tutorial. In this notebook we do what any data scientist does with their data right after parsing it: exploring and understanding different aspect of data. Make sure you understand how we get the songsTable by reading and running the ETL notebook. In the ETL notebook we created and cached a temporary table named songsTable

// Let's quickly do everything to register the tempView of the table here

// fill in comment ... EXERCISE!
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)

def parseLine(line: String): Song = {
  // fill in comment ...
  
  def toDouble(value: String, defaultVal: Double): Double = {
    try {
       value.toDouble
    } catch {
      case e: Exception => defaultVal
    }
  }

  def toInt(value: String, defaultVal: Int): Int = {
    try {
       value.toInt
      } catch {
      case e: Exception => defaultVal
    }
  }
  // fill in comment ...
  val tokens = line.split("\t")
  Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}

// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*") 

// .. fill in comment
val df = dataRDD.map(parseLine).toDF

// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3280] at textFile at command-2972105651606789:30
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
spark.catalog.listTables.show(false) // make sure the temp view of our table is there
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name                                                        |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|sentimentlex_csv                                            |default |null       |EXTERNAL |false      |
|simple_range                                                |default |null       |MANAGED  |false      |
|social_media_usage                                          |default |null       |MANAGED  |false      |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null       |MANAGED  |false      |
|songstable                                                  |null    |null       |TEMPORARY|true       |
+------------------------------------------------------------+--------+-----------+---------+-----------+

A first inspection

A first step to any data exploration is viewing sample data. For this purpose we can use a simple SQL query that returns first 10 rows.

select * from songsTable limit 10
artist_id artist_latitude artist_longitude artist_location artist_name duration end_of_fade_in key key_confidence loudness release song_hotness song_id start_of_fade_out tempo time_signature time_signature_confidence title year partial_sequence
AR81V6H1187FB48872 0.0 0.0 Earl Sixteen 213.7073 0.0 11.0 0.419 -12.106 Soldier of Jah Army 0.0 SOVNZSZ12AB018A9B8 208.289 125.882 1.0 0.0 Rastaman 2003.0 -1.0
ARVVZQP11E2835DBCB 0.0 0.0 Wavves 133.25016 0.0 0.0 0.282 0.596 Wavvves 0.471578247701 SOJTQHQ12A8C143C5F 128.116 89.519 1.0 0.0 I Want To See You (And Go To The Movies) 2009.0 -1.0
ARFG9M11187FB3BBCB 0.0 0.0 Nashua USA C-Side 247.32689 0.0 9.0 0.612 -4.896 Santa Festival Compilation 2008 vol.1 0.0 SOAJSQL12AB0180501 242.196 171.278 5.0 1.0 Loose on the Dancefloor 0.0 225261.0
ARK4Z2O1187FB45FF0 0.0 0.0 Harvest 337.05751 0.247 4.0 0.46 -9.092 Underground Community 0.0 SOTDRVW12AB018BEB9 327.436 84.986 4.0 0.673 No Return 0.0 101619.0
AR4VQSG1187FB57E18 35.25082 -91.74015 Searcy, AR Gossip 430.23628 0.0 2.0 3.4e-2 -6.846 Yr Mangled Heart 0.0 SOTVOCL12A8AE478DD 424.06 121.998 4.0 0.847 Yr Mangled Heart 2006.0 740623.0
ARNBV1X1187B996249 0.0 0.0 Alex 186.80118 0.0 4.0 0.641 -16.108 Jolgaledin 0.0 SODTGRY12AB0182438 166.156 140.735 4.0 5.5e-2 Mariu Sonur Jesus 0.0 673970.0
ARXOEZX1187B9B82A1 0.0 0.0 Elie Attieh 361.89995 0.0 7.0 0.863 -4.919 ELITE 0.0 SOIINTJ12AB0180BA6 354.476 128.024 4.0 0.399 Fe Yom We Leila 0.0 280304.0
ARXPUIA1187B9A32F1 0.0 0.0 Rome, Italy Simone Cristicchi 220.00281 2.119 4.0 0.486 -6.52 Dall'Altra Parte Del Cancello 0.484225272411 SONHXJK12AAF3B5290 214.761 99.954 1.0 0.928 L'Italiano 2007.0 745962.0
ARNPPTH1187B9AD429 51.4855 -0.37196 Heston, Middlesex, England Jimmy Page 156.86485 0.334 7.0 0.493 -9.962 No Introduction Necessary [Deluxe Edition] 0.0 SOGUHGW12A58A80E06 149.269 162.48 4.0 0.534 Wailing Sounds 2004.0 599250.0
AROGWRA122988FEE45 0.0 0.0 Christos Dantis 256.67873 2.537 9.0 0.742 -13.404 Daktilika Apotipomata 0.0 SOJJOYI12A8C13399D 248.912 134.944 4.0 0.162 Stin Proigoumeni Zoi 0.0 611396.0
table("songsTable").printSchema()
root
 |-- artist_id: string (nullable = true)
 |-- artist_latitude: double (nullable = false)
 |-- artist_longitude: double (nullable = false)
 |-- artist_location: string (nullable = true)
 |-- artist_name: string (nullable = true)
 |-- duration: double (nullable = false)
 |-- end_of_fade_in: double (nullable = false)
 |-- key: integer (nullable = false)
 |-- key_confidence: double (nullable = false)
 |-- loudness: double (nullable = false)
 |-- release: string (nullable = true)
 |-- song_hotness: double (nullable = false)
 |-- song_id: string (nullable = true)
 |-- start_of_fade_out: double (nullable = false)
 |-- tempo: double (nullable = false)
 |-- time_signature: double (nullable = false)
 |-- time_signature_confidence: double (nullable = false)
 |-- title: string (nullable = true)
 |-- year: double (nullable = false)
 |-- partial_sequence: integer (nullable = false)
select count(*) from songsTable
count(1)
31369.0
table("songsTable").count() // or equivalently with DataFrame API - recall table("songsTable") is a DataFrame
res4: Long = 31369
display(sqlContext.sql("SELECT duration, year FROM songsTable")) // Aggregation is set to 'Average' in 'Plot Options'
duration year
213.7073 2003.0
133.25016 2009.0
247.32689 0.0
337.05751 0.0
430.23628 2006.0
186.80118 0.0
361.89995 0.0
220.00281 2007.0
156.86485 2004.0
256.67873 0.0
204.64281 0.0
112.48281 0.0
170.39628 0.0
215.95383 0.0
303.62077 0.0
266.60526 0.0
326.19057 1997.0
51.04281 2009.0
129.4624 0.0
253.33506 2003.0
237.76608 2004.0
132.96281 0.0
399.35955 2006.0
168.75057 1991.0
396.042 0.0
192.10404 1968.0
12.2771 2006.0
367.56853 0.0
189.93587 0.0
233.50812 0.0
462.68036 0.0
202.60526 0.0
241.52771 0.0
275.64363 1992.0
350.69342 2007.0
166.55628 1968.0
249.49506 1983.0
53.86404 1992.0
233.76934 2001.0
275.12118 2009.0
191.13751 2006.0
299.07546 0.0
468.74077 0.0
110.34077 0.0
234.78812 2003.0
705.25342 2006.0
383.52934 0.0
196.10077 0.0
299.20608 1998.0
94.04036 0.0
28.08118 2006.0
207.93424 2006.0
152.0322 1999.0
207.96036 2002.0
371.25179 0.0
288.93995 2002.0
235.93751 2004.0
505.70404 0.0
177.57995 0.0
376.842 2004.0
266.84036 2004.0
270.8371 2006.0
178.18077 0.0
527.17669 0.0
244.27057 0.0
436.47955 2006.0
236.79955 0.0
134.53016 2005.0
181.002 0.0
239.41179 1999.0
72.98567 0.0
214.36036 2001.0
150.59546 2007.0
152.45016 1970.0
218.17424 0.0
290.63791 0.0
149.05424 0.0
440.21506 0.0
212.34893 1988.0
278.67383 0.0
269.60934 1974.0
182.69995 2002.0
207.882 2007.0
102.50404 0.0
437.60281 0.0
216.11057 2009.0
193.25342 0.0
234.16118 2009.0
695.77098 0.0
297.58649 1996.0
265.37751 2000.0
182.85669 1990.0
202.23955 0.0
390.08608 2009.0
242.78159 2000.0
242.54649 2002.0
496.66567 2004.0
395.36281 0.0
234.89261 1999.0
237.84444 2005.0
313.57342 2009.0
489.22077 2001.0
239.98649 2004.0
128.65261 0.0
193.07057 0.0
144.19546 0.0
196.96281 2006.0
222.06649 1997.0
58.38322 0.0
346.14812 1998.0
406.54322 0.0
304.09098 2009.0
180.21832 2003.0
213.41995 0.0
323.44771 0.0
54.7522 2009.0
437.02812 1994.0
268.7473 2009.0
104.75057 0.0
248.60689 2006.0
221.41342 0.0
237.81832 1991.0
216.34567 2009.0
78.94159 0.0
47.22893 2005.0
202.00444 2007.0
293.56363 0.0
206.44526 1986.0
267.78077 2003.0
187.27138 2008.0
249.05098 2009.0
221.51791 0.0
452.88444 0.0
163.76118 1992.0
257.17506 0.0
235.78077 0.0
257.82812 1996.0
195.34322 0.0
478.1971 0.0
268.01587 1997.0
136.93342 1983.0
397.53098 0.0
194.69016 2001.0
580.80608 0.0
177.71057 2006.0
257.43628 1999.0
184.13669 0.0
64.57424 2001.0
123.92444 1993.0
257.07057 0.0
219.48036 1996.0
679.41832 0.0
252.29016 1995.0
311.90159 2004.0
252.76036 1998.0
138.94485 0.0
428.64281 0.0
295.31383 0.0
212.03546 0.0
426.50077 0.0
197.11955 0.0
191.55546 0.0
187.53261 2006.0
184.97261 2004.0
388.41424 2009.0
218.90567 0.0
246.49098 0.0
452.88444 0.0
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Exercises

  1. Why do you think average song durations increase dramatically in 70's?
  2. Add error bars with standard deviation around each average point in the plot.
  3. How did average loudness change over time?
  4. How did tempo change over time?
  5. What other aspects of songs can you explore with this technique?

Sampling and visualizing

You can dive deep into Scala visualisations here: - https://docs.databricks.com/notebooks/visualizations/charts-and-graphs-scala.html

You can also use R and Python for visualisations in the same notebook: - https://docs.databricks.com/notebooks/visualizations/index.html

ScaDaMaLe Course site and book

Million Song Dataset - Kaggle Challenge

Predict which songs a user will listen to.

SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.

Stage 3: Modeling Songs via k-means

Model

This is the third step into our project. In the first step we parsed raw text files and created a table. Then we explored different aspects of data and learned that things have been changing over time. In this step we attempt to gain deeper understanding of our data by categorizing (a.k.a. clustering) our data. For the sake of training we pick a fairly simple model based on only three parameters. We leave more sophisticated modeling as exercies to the reader

We pick the most commonly used and simplest clustering algorithm (KMeans) for our job. The SparkML KMeans implementation expects input in a vector column. Fortunately, there are already utilities in SparkML that can help us convert existing columns in our table to a vector field. It is called VectorAssembler. Here we import that functionality and use it to create a new DataFrame

// Let's quickly do everything to register the tempView of the table here

// this is a case class for our row objects
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)

def parseLine(line: String): Song = {
  // this is robust parsing by try-catching type exceptions
  
  def toDouble(value: String, defaultVal: Double): Double = {
    try {
       value.toDouble
    } catch {
      case e: Exception => defaultVal
    }
  }

  def toInt(value: String, defaultVal: Int): Int = {
    try {
       value.toInt
      } catch {
      case e: Exception => defaultVal
    }
  }
  // splitting the sting of each line by the delimiter TAB character '\t'
  val tokens = line.split("\t")
  
  // making song objects
  Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}

// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*") 

// .. fill in comment
val df = dataRDD.map(parseLine).toDF

// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3305] at textFile at command-2972105651606577:32
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
import org.apache.spark.ml.feature.VectorAssembler

val trainingData = new VectorAssembler()
                      .setInputCols(Array("duration", "tempo", "loudness"))
                      .setOutputCol("features")
                      .transform(table("songsTable"))
import org.apache.spark.ml.feature.VectorAssembler
trainingData: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 19 more fields]

All we have done above with the VectorAssembler method is:

  • created a DataFrame called trainingData
  • that transformed our table called songsTable
  • by adding an output column named features using setOutputCol("features")
  • that was obtained from an Array of the songsTable's columns named duration, tempo and loudness using
    • setInputCols(Array("duration", "tempo", "loudness")).
trainingData.take(3) // see first 3 rows of trainingData DataFrame, notice the vectors in the last column
res3: Array[org.apache.spark.sql.Row] = Array([AR81V6H1187FB48872,0.0,0.0,,Earl Sixteen,213.7073,0.0,11,0.419,-12.106,Soldier of Jah Army,0.0,SOVNZSZ12AB018A9B8,208.289,125.882,1.0,0.0,Rastaman,2003.0,-1,[213.7073,125.882,-12.106]], [ARVVZQP11E2835DBCB,0.0,0.0,,Wavves,133.25016,0.0,0,0.282,0.596,Wavvves,0.471578247701,SOJTQHQ12A8C143C5F,128.116,89.519,1.0,0.0,I Want To See You (And Go To The Movies),2009.0,-1,[133.25016,89.519,0.596]], [ARFG9M11187FB3BBCB,0.0,0.0,Nashua USA,C-Side,247.32689,0.0,9,0.612,-4.896,Santa Festival Compilation 2008 vol.1,0.0,SOAJSQL12AB0180501,242.196,171.278,5.0,1.0,Loose on the Dancefloor,0.0,225261,[247.32689,171.278,-4.896]])

Transformers

A Transformer is an abstraction that includes feature transformers and learned models. Technically, a Transformer implements a method transform(), which converts one DataFrame into another, generally by appending one or more columns. For example:

  • A feature transformer might take a DataFrame, read a column (e.g., text), map it into a new column (e.g., feature vectors), and output a new DataFrame with the mapped column appended.
  • A learning model might take a DataFrame, read the column containing feature vectors, predict the label for each feature vector, and output a new DataFrame with predicted labels appended as a column.

Estimators

An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data.

Technically, an Estimator implements a method fit(), which accepts a DataFrame and produces a Model, which is a Transformer.

For example, a learning algorithm such as LogisticRegression is an Estimator, and calling fit() trains a LogisticRegressionModel, which is a Model and hence a Transformer.

display(trainingData.select("duration", "tempo", "loudness", "features").limit(5)) // see features in more detail

Demonstration of the standard algorithm

(1) (2) (3) (4)

  1. k initial "means" (in this case k=3) are randomly generated within the data domain (shown in color).
  • k clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means.
  • The centroid of each of the k clusters becomes the new mean.
  • Steps 2 and 3 are repeated until local convergence has been reached.

The "assignment" step 2 is also referred to as expectation step, the "update step" 3 as maximization step, making this algorithm a variant of the generalized expectation-maximization algorithm.

**Caveats: ** As k-means is a heuristic algorithm, there is no guarantee that it will converge to the global optimum, and the result may depend on the initial clusters. As the algorithm is usually very fast, it is common to run it multiple times with different starting conditions. However, in the worst case, k-means can be very slow to converge. For more details see https://en.wikipedia.org/wiki/K-means_clustering that is also embedded in-place below.

CAUTION!

Iris flower data set, clustered using

  • k-means (left) and
  • true species in the data set (right).

k-means clustering result for the Iris flower data set and actual species visualized using ELKI. Cluster means are marked using larger, semi-transparent symbols.

Note that k-means is non-determinicstic, so results vary. Cluster means are visualized using larger, semi-transparent markers. The visualization was generated using ELKI.

With some cautionary tales we go ahead with applying k-means to our dataset next.

We can now pass this new DataFrame to the KMeans model and ask it to categorize different rows in our data to two different classes (setK(2)). We place the model in a immutable value named model.

Note: This command performs multiple spark jobs (one job per iteration in the KMeans algorithm). You will see the progress bar starting over and over again.

import org.apache.spark.ml.clustering.KMeans
val model = new KMeans().setK(2).fit(trainingData) // 37 seconds in 5 workers cluster
import org.apache.spark.ml.clustering.KMeans
model: org.apache.spark.ml.clustering.KMeansModel = KMeansModel: uid=kmeans_bcf31957c6b5, k=2, distanceMeasure=euclidean, numFeatures=3
//model. // uncomment and place cursor next to . and hit Tab to see all methods on model
model.clusterCenters // get cluster centres
res5: Array[org.apache.spark.ml.linalg.Vector] = Array([208.72069181761958,124.38376303683945,-9.986137113920135], [441.1413218945454,123.00973381818183,-10.560375818181816])
val modelTransformed = model.transform(trainingData) // to get predictions as last column
modelTransformed: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 20 more fields]

Remember that ML Pipelines works with DataFrames. So, our trainingData and modelTransformed are both DataFrames

trainingData.printSchema
root
 |-- artist_id: string (nullable = true)
 |-- artist_latitude: double (nullable = false)
 |-- artist_longitude: double (nullable = false)
 |-- artist_location: string (nullable = true)
 |-- artist_name: string (nullable = true)
 |-- duration: double (nullable = false)
 |-- end_of_fade_in: double (nullable = false)
 |-- key: integer (nullable = false)
 |-- key_confidence: double (nullable = false)
 |-- loudness: double (nullable = false)
 |-- release: string (nullable = true)
 |-- song_hotness: double (nullable = false)
 |-- song_id: string (nullable = true)
 |-- start_of_fade_out: double (nullable = false)
 |-- tempo: double (nullable = false)
 |-- time_signature: double (nullable = false)
 |-- time_signature_confidence: double (nullable = false)
 |-- title: string (nullable = true)
 |-- year: double (nullable = false)
 |-- partial_sequence: integer (nullable = false)
 |-- features: vector (nullable = true)
modelTransformed.printSchema
root
 |-- artist_id: string (nullable = true)
 |-- artist_latitude: double (nullable = false)
 |-- artist_longitude: double (nullable = false)
 |-- artist_location: string (nullable = true)
 |-- artist_name: string (nullable = true)
 |-- duration: double (nullable = false)
 |-- end_of_fade_in: double (nullable = false)
 |-- key: integer (nullable = false)
 |-- key_confidence: double (nullable = false)
 |-- loudness: double (nullable = false)
 |-- release: string (nullable = true)
 |-- song_hotness: double (nullable = false)
 |-- song_id: string (nullable = true)
 |-- start_of_fade_out: double (nullable = false)
 |-- tempo: double (nullable = false)
 |-- time_signature: double (nullable = false)
 |-- time_signature_confidence: double (nullable = false)
 |-- title: string (nullable = true)
 |-- year: double (nullable = false)
 |-- partial_sequence: integer (nullable = false)
 |-- features: vector (nullable = true)
 |-- prediction: integer (nullable = false)
  • The column features that we specified as output column to our VectorAssembler contains the features
  • The new column prediction in modelTransformed contains the predicted output
val transformed = modelTransformed.select("duration", "tempo", "loudness", "prediction")
transformed: org.apache.spark.sql.DataFrame = [duration: double, tempo: double ... 2 more fields]

To comfortably visualize the data we produce a random sample. Remember the display() function? We can use it to produce a nicely rendered table of transformed DataFrame.

// just sampling the fraction 0.005 of all the rows at random, 
// 'false' argument to sample is for sampling without replacement
display(transformed.sample(false, fraction = 0.005, 12988934L)) 
duration tempo loudness prediction
265.97832 129.987 -5.065 0.0
306.36363 127.529 -8.273 0.0
195.73506 115.191 -6.327 0.0
257.82812 92.892 -13.621 0.0
177.55383 149.853 -10.969 0.0
224.9922 97.98 -5.921 0.0
528.09098 160.022 -5.956 1.0
213.68118 100.219 -4.528 0.0
35.52608 121.349 -18.721 0.0
213.68118 120.581 -10.633 0.0
300.53832 139.017 -13.319 0.0
138.81424 160.044 -11.138 0.0
413.962 89.588 -13.143 1.0
292.17914 188.543 -5.389 0.0
419.34322 140.008 -5.389 1.0
261.61587 152.951 -12.447 0.0
168.64608 99.617 -8.164 0.0
446.27546 196.005 -6.621 1.0
343.92771 122.877 -10.264 1.0
261.69424 145.129 -4.733 0.0
408.47628 61.259 -11.244 1.0
243.3824 161.778 -6.88 0.0
184.00608 111.461 -9.788 0.0
67.39546 188.723 -3.239 0.0
165.51138 81.125 -18.692 0.0
214.85669 144.174 -17.989 0.0
185.96526 186.897 -6.041 0.0
170.05669 75.356 -16.486 0.0
193.72363 117.131 -9.822 0.0
227.23873 91.953 -11.221 0.0
177.6322 150.962 -7.288 0.0
117.21098 100.021 -10.782 0.0
422.42567 91.214 -8.872 1.0
145.162 95.388 -10.166 0.0
242.93832 112.015 -7.135 0.0
366.00118 65.05 -13.768 1.0
601.15546 70.623 -11.179 1.0
196.17914 121.563 -5.434 0.0
399.35955 124.963 -3.415 1.0
287.73832 161.648 -14.721 0.0
285.09995 132.86 -10.23 0.0
358.32118 132.874 -6.071 1.0
407.71873 85.996 -10.103 1.0
463.25506 95.083 -6.4 1.0
295.91465 93.515 -8.987 0.0
273.8673 190.044 -10.556 0.0
280.73751 113.363 -6.893 0.0
142.65424 206.067 -7.996 0.0
197.38077 84.023 -10.964 0.0
226.24608 166.768 -5.941 0.0
152.47628 153.528 -7.393 0.0
315.32363 139.919 -7.438 0.0
329.03791 180.059 -6.473 1.0
296.88118 137.976 -5.49 0.0
250.77506 84.543 -18.058 0.0
48.14322 52.814 -11.617 0.0
290.16771 141.24 -14.009 0.0
406.04689 133.997 -9.685 1.0
124.57751 161.237 -12.314 0.0
280.45016 154.003 -8.094 0.0
472.31955 219.371 -6.08 1.0
267.4673 152.123 -18.457 0.0
216.21506 88.022 -7.096 0.0
31.00689 237.737 -11.538 0.0
283.21914 132.934 -11.153 0.0
284.21179 110.585 -15.384 0.0
154.87955 157.881 -4.377 0.0
431.46404 113.905 -8.651 1.0
215.87546 124.572 -7.67 0.0
161.43628 43.884 -14.936 0.0
29.09995 135.146 -1.837 0.0
301.68771 123.672 -9.293 0.0
170.10893 124.022 -9.589 0.0
281.96526 63.544 -8.25 0.0
484.54485 149.565 -5.501 1.0
135.1571 92.749 -7.881 0.0
180.45342 137.899 -5.246 0.0
142.44526 85.406 -11.305 0.0
209.3971 93.233 -13.079 0.0
319.9473 122.936 -5.98 0.0
55.82322 249.325 -8.656 0.0
192.88771 124.006 -5.745 0.0
624.14322 128.988 -7.872 1.0
188.49914 107.264 -13.24 0.0
148.32281 150.075 -5.793 0.0
195.00363 49.31 -14.54 0.0
130.61179 100.208 -10.487 0.0
212.11383 111.048 -4.749 0.0
204.56444 92.076 -7.906 0.0
292.04853 135.932 -9.004 0.0
377.83465 73.336 -13.457 1.0
246.96118 116.761 -8.117 0.0
90.30485 39.3 -15.444 0.0
201.92608 123.558 -9.857 0.0
282.06975 136.09 -6.202 0.0
194.55955 119.488 -16.454 0.0
318.4322 140.467 -6.375 0.0
55.45751 121.476 -7.891 0.0
275.30404 90.024 -6.191 0.0
422.24281 131.994 -8.786 1.0
269.47873 88.921 -4.419 0.0
186.01751 55.271 -16.153 0.0
221.17832 94.967 -6.591 0.0
275.66975 94.999 -16.843 0.0
335.28118 90.129 -13.33 1.0
288.88771 102.96 -10.967 0.0
190.71955 88.269 -13.167 0.0
211.17342 126.741 -10.398 0.0
116.92363 140.09 -7.578 0.0
310.282 181.753 -17.61 0.0
184.21506 112.336 -8.764 0.0
248.21506 101.988 -6.487 0.0
116.00934 84.793 -9.057 0.0
299.88526 143.66 -7.279 0.0
1376.78322 107.066 -7.893 1.0
182.85669 191.559 -10.367 0.0
238.2624 48.592 -9.687 0.0
239.5424 105.47 -9.258 0.0
195.16036 127.911 -7.054 0.0
149.73342 118.643 -10.526 0.0
424.30649 62.8 -22.35 1.0
221.77914 104.983 -7.908 0.0
407.7971 126.983 -11.675 1.0
175.04608 120.542 -17.484 0.0
306.65098 120.0 -18.973 0.0
315.0624 85.82 -11.858 0.0
88.5024 211.429 -11.543 0.0
101.61587 19.215 -26.402 0.0
132.04853 186.123 -7.21 0.0
146.25914 200.16 -15.88 0.0
147.3824 104.658 -8.836 0.0
156.26404 135.353 -8.245 0.0
66.2722 115.129 -16.416 0.0
247.43138 124.991 -11.79 0.0
282.90567 115.472 -12.448 0.0
401.05751 125.015 -9.465 1.0
156.08118 88.128 -16.916 0.0
198.08608 133.961 -8.055 0.0
214.09914 84.353 -12.991 0.0
269.73995 161.953 -3.091 0.0
199.44444 169.922 -6.241 0.0
149.65506 190.802 -30.719 0.0
227.23873 120.14 -12.39 0.0

To generate a scatter plot matrix, click on the plot button bellow the table and select scatter. That will transform your table to a scatter plot matrix. It automatically picks all numeric columns as values. To include predicted clusters, click on Plot Options and drag prediction to the list of Keys. You will get the following plot. On the diagonal panels you see the PDF of marginal distribution of each variable. Non-diagonal panels show a scatter plot between variables of the two variables of the row and column. For example the top right panel shows the scatter plot between duration and loudness. Each point is colored according to the cluster it is assigned to.

display(transformed.sample(false, fraction = 0.01)) // try fraction=1.0 as this dataset is small
duration tempo loudness prediction
189.93587 134.957 -12.934 0.0
206.44526 135.26 -11.146 0.0
295.65342 137.904 -7.308 0.0
158.1971 174.831 -12.544 0.0
259.99628 171.094 -12.057 0.0
277.002 88.145 -6.039 0.0
1518.65424 65.003 -19.182 1.0
121.73016 85.041 -8.193 0.0
205.97506 106.952 -6.572 0.0
206.18404 89.447 -9.535 0.0
202.52689 134.13 -6.535 0.0
259.02975 97.948 -11.276 0.0
308.53179 80.017 -12.277 0.0
151.71873 117.179 -12.394 0.0
165.56363 99.187 -11.414 0.0
370.83383 67.495 -20.449 1.0
238.65424 100.071 -9.276 0.0
163.7873 119.924 -5.941 0.0
279.32689 67.371 -26.67 0.0
353.85424 68.405 -8.105 1.0
227.23873 117.62 -7.874 0.0
195.49995 85.916 -8.828 0.0
322.45506 135.826 -5.034 0.0
235.88526 109.773 -10.919 0.0
235.15383 163.924 -9.666 0.0
253.67465 125.014 -7.171 0.0
415.03302 71.991 -15.224 1.0
190.85016 152.805 -2.871 0.0
290.76853 84.841 -6.161 0.0
346.8273 129.981 -10.377 1.0
174.70649 143.446 -11.994 0.0
170.84036 70.033 -7.782 0.0
517.66812 151.948 -12.363 1.0
62.06649 154.406 -9.387 0.0
505.96526 90.59 -19.848 1.0
480.86159 0.0 -11.707 1.0
330.13506 204.534 -9.85 1.0
223.99955 110.3 -12.339 0.0
200.98567 89.215 -3.858 0.0
158.09261 178.826 -13.681 0.0
186.46159 110.102 -16.477 0.0
399.46404 125.03 -10.786 1.0
118.64771 85.747 -34.461 0.0
352.49587 91.139 -9.13 1.0
150.15138 106.364 -7.337 0.0
158.98077 124.54 -17.405 0.0
122.17424 170.018 -9.047 0.0
299.38893 91.976 -6.266 0.0
197.95546 142.744 -8.189 0.0
332.38159 89.071 -5.08 1.0
309.78567 136.05 -15.105 0.0
198.47791 82.546 -12.594 0.0
254.17098 144.01 -9.0 0.0
155.81995 233.022 -8.978 0.0
176.53506 136.255 -9.675 0.0
331.54567 221.511 -13.778 1.0
140.72118 162.44 -10.654 0.0
210.9122 120.039 -5.391 0.0
197.27628 119.396 -22.599 0.0
339.25179 96.842 -16.207 1.0
355.97016 142.777 -5.118 1.0
170.23955 55.107 -17.654 0.0
360.01914 119.997 -7.53 1.0
241.76281 101.938 -3.765 0.0
84.92363 236.863 -15.846 0.0
361.09016 120.121 -5.861 1.0
262.37342 89.924 -4.819 0.0
225.74975 139.994 -11.505 0.0
246.9873 172.147 -16.273 0.0
159.242 166.855 -4.771 0.0
192.86159 148.297 -13.423 0.0
140.90404 92.602 -12.604 0.0
224.20853 71.879 -16.615 0.0
235.78077 180.366 -3.918 0.0
244.45342 105.077 -5.004 0.0
210.57261 200.044 -11.381 0.0
327.3922 112.023 -11.341 1.0
168.38485 99.991 -4.1 0.0
287.13751 137.154 -12.948 0.0
148.63628 121.773 -11.432 0.0
214.7522 124.437 -5.834 0.0
197.43302 92.959 -4.948 0.0
150.17751 112.0 -5.968 0.0
152.86812 120.884 -15.693 0.0
173.322 120.827 -7.96 0.0
235.15383 85.519 -8.172 0.0
299.41506 94.68 -15.486 0.0
222.35383 156.046 -8.885 0.0
235.85914 130.055 -5.341 0.0
276.71465 170.059 -2.926 0.0
136.07138 168.895 -6.971 0.0
205.7922 129.57 -6.113 0.0
268.72118 126.894 -7.142 0.0
390.63465 232.08 -5.493 1.0
242.59873 124.034 -11.24 0.0
274.80771 108.178 -21.456 0.0
157.25669 151.108 -11.678 0.0
203.67628 140.02 -5.939 0.0
302.47138 148.2 -10.428 0.0
292.64934 106.006 -3.538 0.0
245.002 135.997 -10.6 0.0
181.57669 132.89 -5.429 0.0
36.38812 65.132 -12.621 0.0
656.14322 95.469 -8.588 1.0
262.50404 88.025 -4.359 0.0
261.95546 117.93 -5.5 0.0
337.57995 122.08 -6.658 1.0
373.08036 126.846 -10.115 1.0
220.36853 157.934 -13.192 0.0
357.22404 199.889 -9.832 1.0
663.61424 143.681 -9.066 1.0
155.08853 201.152 -7.43 0.0
264.56771 133.7 -11.767 0.0
262.53016 105.041 -3.933 0.0
386.61179 132.644 -8.34 1.0
224.41751 93.333 -12.001 0.0
264.25424 117.547 -6.134 0.0
12.82567 99.229 -29.527 0.0
470.25587 135.99 -7.403 1.0
213.7073 92.584 -8.398 0.0
50.59873 108.989 -10.006 0.0
273.162 114.014 -8.527 0.0
285.1522 101.877 -9.361 0.0
267.88526 123.903 -10.177 0.0
334.54975 138.386 -7.389 1.0
174.0273 95.084 -21.944 0.0
242.15465 87.196 -9.749 0.0
188.96934 188.967 -3.288 0.0
480.67873 127.989 -7.243 1.0
182.54322 72.894 -17.837 0.0
231.1571 126.959 -5.632 0.0
105.37751 136.156 -13.607 0.0
125.88363 93.022 -13.496 0.0
216.99873 151.866 -7.47 0.0
176.50893 142.601 -3.881 0.0
245.57669 123.186 -3.609 0.0
191.37261 145.008 -14.3 0.0
62.11873 80.961 -12.453 0.0
274.80771 83.505 -10.032 0.0
148.29669 92.154 -13.542 0.0
241.47546 115.886 -11.948 0.0
250.40934 100.001 -18.335 0.0
154.56608 96.464 -12.133 0.0
485.14567 84.633 -8.812 1.0
307.22567 63.498 -9.047 0.0
167.78404 122.92 -13.528 0.0
345.10322 130.003 -7.858 1.0
178.9122 111.13 -7.33 0.0
237.29587 111.244 -15.209 0.0
230.16444 74.335 -12.244 0.0
230.50404 98.933 -12.616 0.0
195.16036 131.037 -12.769 0.0
130.01098 99.176 -11.082 0.0
150.02077 97.501 -2.945 0.0
239.64689 89.99 -9.224 0.0
91.6371 71.187 -16.523 0.0
136.88118 121.19 -12.181 0.0
265.29914 167.132 -13.859 0.0
166.84363 76.298 -7.692 0.0
34.79465 109.386 -13.591 0.0
344.29342 138.826 -12.746 1.0
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displayHTML(frameIt("https://en.wikipedia.org/wiki/Euclidean_space",500)) // make sure you run the cell with first wikipedia article!

Do you see the problem in our clusters based on the plot?

As you can see there is very little "separation" (in the sense of being separable into two point clouds, that represent our two identifed clusters, such that they have minimal overlay of these two features, i.e. tempo and loudness. NOTE that this sense of "pairwise separation" is a 2D projection of all three features in 3D Euclidean Space, i.e. loudness, tempo and duration, that depends directly on their two-dimensional visually sense-making projection of perhaps two important song features, as depicted in the corresponding 2D-scatter-plot of tempo versus loudness within the 2D scatter plot matrix that is helping us to partially visualize in the 2D-plane all of the three features in the three dimensional real-valued feature space that was the input to our K-Means algorithm) between loudness, and tempo and generated clusters. To see that, focus on the panels in the first and second columns of the scatter plot matrix. For varying values of loudness and tempo prediction does not change. Instead, duration of a song alone predicts what cluster it belongs to. Why is that?

To see the reason, let's take a look at the marginal distribution of duration in the next cell.

To produce this plot, we have picked histogram from the plots menu and in Plot Options we chose prediction as key and duration as value. The histogram plot shows significant skew in duration. Basically there are a few very long songs. These data points have large leverage on the mean function (what KMeans uses for clustering).

display(transformed.sample(false, fraction = 1.0).select("duration", "prediction")) // plotting over all results
duration prediction
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There are known techniques for dealing with skewed features. A simple technique is applying a power transformation. We are going to use the simplest and most common power transformation: logarithm.

In following cell we repeat the clustering experiment with a transformed DataFrame that includes a new column called log_duration.

val df = table("songsTable").selectExpr("tempo", "loudness", "log(duration) as log_duration")
val trainingData2 = new VectorAssembler().
                  setInputCols(Array("log_duration", "tempo", "loudness")).
                  setOutputCol("features").
                  transform(df)
val model2 = new KMeans().setK(2).fit(trainingData2)
val transformed2 = model2.transform(trainingData2).select("log_duration", "tempo", "loudness", "prediction")
display(transformed2.sample(false, fraction = 0.1))
log_duration tempo loudness prediction
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5.689011180946999 82.231 -10.715 1.0
5.230324459215954 111.427 -10.227 1.0
4.656557165987008 100.884 -13.622 1.0
5.811300920307404 96.936 -12.364 1.0
5.272080007701286 178.346 -9.121 0.0
5.90413425563417 118.954 -11.949 1.0
5.623930390479518 93.039 -9.164 1.0
6.035007636090424 98.209 -7.722 1.0
5.172927427877826 113.95 -11.716 1.0
5.409075115901 130.816 -7.251 0.0
5.586748495886647 58.68 -13.852 1.0
5.53749315203344 174.995 -6.466 0.0
5.982509141902109 139.895 -7.423 0.0
5.654579205170516 197.992 -7.655 0.0
4.9389533818061935 190.038 -6.257 0.0
5.696757691877665 141.007 -24.066 0.0
5.139811725963339 139.774 -5.324 0.0

The new clustering model makes much more sense. Songs with high tempo and loudness are put in one cluster and song duration does not affect song categories.

To really understand how the points in 3D behave you need to see them in 3D interactively and understand the limits of its three 2D projections. For this let us spend some time and play in sageMath Worksheet in CoCalc (it is free for light-weight use and perhaps worth the 7 USD a month if you need more serious computing in mathmeatics, statistics, etc. in multiple languages!).

Let us take a look at this sageMath Worksheet published here:

The point of the above little example is that you need to be able to tell a sensible story with your data science process and not just blindly apply a heuristic, but highly scalable, algorithm which depends on the notion of nearest neighborhoods defined by the metric (Euclidean distances in 3-dimensional real-valued spaces in this example) induced by the features you have engineered or have the power to re/re/...-engineer to increase the meaningfullness of the problem at hand.

Determining Optimal K

There are methods to find the optimal number of clusters. This partly depends on the purpose of the unsupervised learning task.

See here for some metrics in scala for the Irish dataset.

ScaDaMaLe Course site and book

Supervised Clustering with Decision Trees

Visual Introduction to decision trees and application to hand-written digit recognition

SOURCE: This is just a couple of decorations on a notebook published in databricks community edition in 2016.

Decision Trees for handwritten digit recognition

This notebook demonstrates learning a Decision Tree using Spark's distributed implementation. It gives the reader a better understanding of some critical hyperparameters for the tree learning algorithm, using examples to demonstrate how tuning the hyperparameters can improve accuracy.

Background: To learn more about Decision Trees, check out the resources at the end of this notebook. The visual description of ML and Decision Trees provides nice intuition helpful to understand this notebook, and Wikipedia gives lots of details.

Data: We use the classic MNIST handwritten digit recognition dataset. It is from LeCun et al. (1998) and may be found under "mnist" at the LibSVM dataset page.

Goal: Our goal for our data is to learn how to recognize digits (0 - 9) from images of handwriting. However, we will focus on understanding trees, not on this particular learning problem.

Takeaways: Decision Trees take several hyperparameters which can affect the accuracy of the learned model. There is no one "best" setting for these for all datasets. To get the optimal accuracy, we need to tune these hyperparameters based on our data.

Let's Build Intuition for Learning with Decision Trees

Load MNIST training and test datasets

Our datasets are vectors of pixels representing images of handwritten digits. For example:

Image of a digit Image of all 10 digits

These datasets are stored in the popular LibSVM dataset format. We will load them using MLlib's LibSVM dataset reader utility.

//-----------------------------------------------------------------------------------------------------------------
// using RDD-based MLlib - ok for Spark 1.x
// MLUtils.loadLibSVMFile returns an RDD.
//import org.apache.spark.mllib.util.MLUtils
//val trainingRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
//val testRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// We convert the RDDs to DataFrames to use with ML Pipelines.
//val training = trainingRDD.toDF()
//val test = testRDD.toDF()
// Note: In Spark 1.6 and later versions, Spark SQL has a LibSVM data source.  The above lines can be simplified to:
//// val training = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-train.txt")
//// val test = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-test.txt")
//-----------------------------------------------------------------------------------------------------------------
val training = spark.read.format("libsvm")
                    .option("numFeatures", "780")
                    .load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")

val test = spark.read.format("libsvm")
                    .option("numFeatures", "780")
                    .load("/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// Cache data for multiple uses.
training.cache()
test.cache()

println(s"We have ${training.count} training images and ${test.count} test images.")
We have 60000 training images and 10000 test images.
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
test: org.apache.spark.sql.DataFrame = [label: double, features: vector]

Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).

training.printSchema()
root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)
training.show(3) // replace 'true' by 'false' to see the whole row hidden by '...'
+-----+--------------------+
|label|            features|
+-----+--------------------+
|  5.0|(780,[152,153,154...|
|  0.0|(780,[127,128,129...|
|  4.0|(780,[160,161,162...|
+-----+--------------------+
only showing top 3 rows
display(training) // this is databricks-specific for interactive visual convenience

The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) or training.show(2,false) above, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):

type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]

Here,

  • type: 0 says we have a sparse vector that only represents non-zero entries (as opposed to a dense vector where every entry is represented).
  • size: 780 says the vector has 780 indices in total
  • these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
  • indices: [152,153,155,...,682,683] are the indices from the [0,1,...,779] possible indices with non-zero values
    • a value is an integer encoding the gray-level at the pixel index
  • values: [3, 18, 18,18,126,...,132,16] are the actual gray level values, for example:
    • at pixed index 152 the gray-level value is 3,
    • at index 153 the gray-level value is 18,
    • ..., and finally at
    • at index 683 the gray-level value is 18

Train a Decision Tree

We begin by training a decision tree using the default settings. Before training, we want to tell the algorithm that the labels are categories 0-9, rather than continuous values. We use the StringIndexer class to do this. We tie this feature preprocessing together with the tree algorithm using a Pipeline. ML Pipelines are tools Spark provides for piecing together Machine Learning algorithms into workflows. To learn more about Pipelines, check out other ML example notebooks in Databricks and the ML Pipelines user guide. Also See mllib-decision-tree.html#basic-algorithm.

// Import the ML algorithms we will use.
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
// StringIndexer: Read input column "label" (digits) and annotate them as categorical values.
val indexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel")

// DecisionTreeClassifier: Learn to predict column "indexedLabel" using the "features" column.
val dtc = new DecisionTreeClassifier().setLabelCol("indexedLabel")

// Chain indexer + dtc together into a single ML Pipeline.
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
indexer: org.apache.spark.ml.feature.StringIndexer = strIdx_5e4c4bafd052
dtc: org.apache.spark.ml.classification.DecisionTreeClassifier = dtc_6916f99c66b5
pipeline: org.apache.spark.ml.Pipeline = pipeline_7c1c5306008c

Now, let's fit a model to our data.

val model = pipeline.fit(training)
model: org.apache.spark.ml.PipelineModel = pipeline_7c1c5306008c

We can inspect the learned tree by displaying it using Databricks ML visualization. (Visualization is available for several but not all models.)

// The tree is the last stage of the Pipeline.  Display it!
val tree = model.stages.last.asInstanceOf[DecisionTreeClassificationModel]
display(tree)
treeNode
{"index":31,"featureType":"continuous","prediction":null,"threshold":141.5,"categories":null,"feature":350,"overflow":false}
{"index":15,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":568,"overflow":false}
{"index":7,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":430,"overflow":false}
{"index":3,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":405,"overflow":false}
{"index":1,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":484,"overflow":false}
{"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":2,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":5,"featureType":"continuous","prediction":null,"threshold":14.5,"categories":null,"feature":516,"overflow":false}
{"index":4,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":6,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":11,"featureType":"continuous","prediction":null,"threshold":7.5,"categories":null,"feature":211,"overflow":false}
{"index":9,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":98,"overflow":false}
{"index":8,"featureType":null,"prediction":8.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":10,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":13,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":156,"overflow":false}
{"index":12,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":14,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":23,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":435,"overflow":false}
{"index":19,"featureType":"continuous","prediction":null,"threshold":10.5,"categories":null,"feature":489,"overflow":false}
{"index":17,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":351,"overflow":false}
{"index":16,"featureType":null,"prediction":5.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":18,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":21,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":320,"overflow":false}
{"index":20,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":22,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":27,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":347,"overflow":false}
{"index":25,"featureType":"continuous","prediction":null,"threshold":1.5,"categories":null,"feature":344,"overflow":false}
{"index":24,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":26,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":29,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":655,"overflow":false}
{"index":28,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":30,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":47,"featureType":"continuous","prediction":null,"threshold":44.5,"categories":null,"feature":489,"overflow":false}
{"index":39,"featureType":"continuous","prediction":null,"threshold":41.5,"categories":null,"feature":290,"overflow":false}
{"index":35,"featureType":"continuous","prediction":null,"threshold":77.5,"categories":null,"feature":486,"overflow":false}
{"index":33,"featureType":"continuous","prediction":null,"threshold":113.5,"categories":null,"feature":490,"overflow":false}
{"index":32,"featureType":null,"prediction":2.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":34,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":37,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":656,"overflow":false}
{"index":36,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":38,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":43,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":297,"overflow":false}
{"index":41,"featureType":"continuous","prediction":null,"threshold":178.5,"categories":null,"feature":486,"overflow":false}
{"index":40,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":42,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":45,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":514,"overflow":false}
{"index":44,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":46,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":55,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":234,"overflow":false}
{"index":51,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":402,"overflow":false}
{"index":49,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":300,"overflow":false}
{"index":48,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":50,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":53,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":103,"overflow":false}
{"index":52,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":54,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":59,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":658,"overflow":false}
{"index":57,"featureType":"continuous","prediction":null,"threshold":24.5,"categories":null,"feature":345,"overflow":false}
{"index":56,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":58,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":60,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false}

Above, we can see how the tree makes predictions. When classifying a new example, the tree starts at the "root" node (at the top). Each tree node tests a pixel value and goes either left or right. At the bottom "leaf" nodes, the tree predicts a digit as the image's label.

Hyperparameter Tuning

Run the next cell and come back into hyper-parameter tuning for a couple minutes.

Exploring "maxDepth": training trees of different sizes

In this section, we test tuning a single hyperparameter maxDepth, which determines how deep (and large) the tree can be. We will train trees at varying depths and see how it affects the accuracy on our held-out test set.

Note: The next cell can take about 1 minute to run since it is training several trees which get deeper and deeper.

val variedMaxDepthModels = (0 until 8).map { maxDepth =>
  // For this setting of maxDepth, learn a decision tree.
  dtc.setMaxDepth(maxDepth)
  // Create a Pipeline with our feature processing stage (indexer) plus the tree algorithm
  val pipeline = new Pipeline().setStages(Array(indexer, dtc))
  // Run the ML Pipeline to learn a tree.
  pipeline.fit(training)
}
variedMaxDepthModels: scala.collection.immutable.IndexedSeq[org.apache.spark.ml.PipelineModel] = Vector(pipeline_310f2d2f2acc, pipeline_cf295c016b8e, pipeline_54e998e43742, pipeline_043069dd1cac, pipeline_d1c3a168b215, pipeline_7440ffd2443d, pipeline_ea992a1dcc18, pipeline_048b83baaf8e)

We will use the default metric to evaluate the performance of our classifier:

// Define an evaluation metric.  In this case, we will use "accuracy".
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setMetricName("f1") // default MetricName
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_763f80cd3db6, metricName=f1, metricLabel=0.0, beta=1.0, eps=1.0E-15
// For each maxDepth setting, make predictions on the test data, and compute the classifier's f1 performance metric.
val f1MetricPerformanceMeasures = (0 until 8).map { maxDepth =>
  val model = variedMaxDepthModels(maxDepth)
  // Calling transform() on the test set runs the fitted pipeline.
  // The learned model makes predictions on each test example.
  val predictions = model.transform(test)
  // Calling evaluate() on the predictions DataFrame computes our performance metric.
  (maxDepth, evaluator.evaluate(predictions))
}.toDF("maxDepth", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxDepth: int, f1: double]

We can display our accuracy results and see immediately that deeper, larger trees are more powerful classifiers, achieving higher accuracies.

Note: When you run f1MetricPerformanceMeasures.show(), you will get a table with f1 score getting better (i.e., approaching 1) with depth.

f1MetricPerformanceMeasures.show()
+--------+-------------------+
|maxDepth|                 f1|
+--------+-------------------+
|       0|  0.023138302649304|
|       1|0.07724539080137968|
|       2|0.21450119544051488|
|       3| 0.4328937409827488|
|       4| 0.5851464721123918|
|       5|  0.680614417707604|
|       6|  0.752106383232933|
|       7| 0.7875867636754111|
+--------+-------------------+

Even though deeper trees are more powerful, they are not always better (recall from the SF/NYC city classification from house features at The visual description of ML and Decision Trees). If we kept increasing the depth on a rich enough dataset, training would take longer and longer. We also might risk overfitting (fitting the training data so well that our predictions get worse on test data); it is important to tune parameters based on held-out data to prevent overfitting. This will ensure that the fitted model generalizes well to yet unseen data, i.e. minimizes generalization error in a mathematical statistical sense.

Exploring "maxBins": discretization for efficient distributed computing

This section explores a more expert-level setting maxBins. For efficient distributed training of Decision Trees, Spark and most other libraries discretize (or "bin") continuous features (such as pixel values) into a finite number of values. This is an important step for the distributed implementation, but it introduces a tradeoff: Larger maxBins mean your data will be more accurately represented, but it will also mean more communication (and slower training).

The default value of maxBins generally works, but it is interesting to explore on our handwritten digit dataset. Remember our digit image from above:

Image of a digit

It is grayscale. But if we set maxBins = 2, then we are effectively making it a black-and-white image, not grayscale. Will that affect the accuracy of our model? Let's see!

Note: The next cell can take about 35 seconds to run since it trains several trees. Read the details on maxBins at mllib-decision-tree.html#split-candidates.

dtc.setMaxDepth(6) // Set maxDepth to a reasonable value.
// now try the maxBins "hyper-parameter" which actually acts as a "coarsener" 
//     mathematical researchers should note that it is a sub-algebra of the finite 
//     algebra of observable pixel images at the finest resolution available to us
// giving a compression of the image to fewer coarsely represented pixels
val f1MetricPerformanceMeasures = Seq(2, 4, 8, 16, 32).map { case maxBins =>
  // For this value of maxBins, learn a tree.
  dtc.setMaxBins(maxBins)
  val pipeline = new Pipeline().setStages(Array(indexer, dtc))
  val model = pipeline.fit(training)
  // Make predictions on test data, and compute accuracy.
  val predictions = model.transform(test)
  (maxBins, evaluator.evaluate(predictions))
}.toDF("maxBins", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxBins: int, f1: double]
f1MetricPerformanceMeasures.show()
+-------+------------------+
|maxBins|                f1|
+-------+------------------+
|      2|0.7400788627816512|
|      4|0.7389031841129898|
|      8|0.7442844661661937|
|     16|0.7447853737964406|
|     32| 0.752106383232933|
+-------+------------------+

We can see that extreme discretization (black and white) hurts performance as measured by F1-error, but only a bit. Using more bins increases the accuracy (but also makes learning more costly).

What's next?

  • Explore: Try out tuning other parameters of trees---or even ensembles like Random Forests or Gradient-Boosted Trees.
  • Automated tuning: This type of tuning does not have to be done by hand. (We did it by hand here to show the effects of tuning in detail.) MLlib provides automated tuning functionality via CrossValidator. Check out the other Databricks ML Pipeline guides or the Spark ML user guide for details.

Resources

If you are interested in learning more on these topics, these resources can get you started:

ScaDaMaLe Course site and book

Linear Algebra Review / re-Introduction

by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

This is a breeze'y scalarific break-down of:

Using the above resources we'll provide a review of basic linear algebra concepts that will recur throughout the course. These concepts include:

  1. Matrices
  • Vectors
  • Arithmetic operations with vectors and matrices

We will see the accompanying Scala computations in the local or non-distributed setting.

Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications, eigen systems with real and comples Eigen values NOW from the following interactive visual-cognitive aid at:

Breeze is a linear algebra package in Scala. First, let us import it as follows:

import breeze.linalg._
import breeze.linalg._

1. Matrix: creation and element-access

A matrix is a two-dimensional array.

Let us denote matrices via bold uppercase letters as follows:

For instance, the matrix below is denoted with \(\mathbf{A}\), a capital bold A.

\[ \mathbf{A} = \begin{pmatrix} a_{11} & a_{12} & a_{13} \ a_{21} & a_{22} & a_{23} \ a_{31} & a_{32} & a_{33} \end{pmatrix} \]

We usually put commas between the row and column indexing sub-scripts, to make the possibly multi-digit indices distinguishable as follows:

\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \ a_{3,1} & a_{3,2} & a_{3,3} \end{pmatrix} \]

  • \(\mathbf{A}_{i,j}\) denotes the entry in \(i\)-th row and \(j\)-th column of the matrix \(\mathbf{A}\).
  • So for instance,
    • the first entry, the top left entry, is denoted by \(\mathbf{A}_{1,1}\).
    • And the entry in the third row and second column is denoted by \(\mathbf{A}_{3,2}\).
    • We say that a matrix with n rows and m columns is an \(n\) by \(m\) matrix and written as \(n \times m\)
      • The matrix \(\mathbf{A}\) shown above is a generic \(3 \times 3\) (pronounced 3-by-3) matrix.
      • And the matrix in Ameet's example in the video above, having 4 rows and 3 columns, is a 4 by 3 matrix.
    • If a matrix \(\mathbf{A}\) is \(n \times m\), we write:
      • \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
        • where, \(\mathbb{R}\) here denotes the set of all real numbers in the line given by the open interval: \((-\infty,+\infty)\).

Let us created a matrix A as a val (that is immutable) in scala. The matrix we want to create is mathematically notated as follows:

\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \end{pmatrix}

\begin{pmatrix} 1 & 2 & 3 \ 4 & 5 & 6 \end{pmatrix} \]

val A = DenseMatrix((1, 2, 3), (4, 5, 6)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1  2  3
4  5  6
A.size
res0: Int = 6
A.rows // number of rows
res1: Int = 2
A.size / A.rows // num of columns
res2: Int = 3
A.cols // also say
res3: Int = 3

Now, let's access the element \(a_{1,1}\), i.e., the element from the first row and first column of \(\mathbf{A}\), which in our val A matrix is the integer of type Int equalling 1.

A(0, 0) // Remember elements are indexed by zero in scala
res4: Int = 1

Gotcha: indices in breeze matrices start at 0 as in numpy of python and not at 1 as in MATLAB!

Of course if you assign the same dense matrix to a mutable var B then its entries can be modified as follows:

var B = DenseMatrix((1, 2, 3), (4, 5, 6))
B: breeze.linalg.DenseMatrix[Int] =
1  2  3
4  5  6
B(0,0)=999; B(1,1)=969; B(0,2)=666
B
res5: breeze.linalg.DenseMatrix[Int] =
999  2    666
4    969  6

Vector

(watch now 0:31 = 62-93 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

  • A vector is a matrix with many rows and one column.

  • We'll denote a vector by bold lowercase letters: \[\mathbf{a} = \begin{pmatrix} 3.3 \ 1.0 \ 6.3 \ 3.6 \end{pmatrix}\]

    So, the vector above is denoted by \(\mathbf{a}\), the lowercase, bold a.

  • \(a_i\) denotes the i-th entry of a vector. So for instance:

    • \(a_2\) denotes the second entry of the vector and it is 1.0 for our vector.
  • If a vector is m-dimensional, then we say that \(\mathbf{a}\) is in \(\mathbb{R}^m\) and write \(\mathbf{a} \in \ \mathbb{R}^m\).

    • So our \(\mathbf{a} \in \ \mathbb{R}^4\).
val a = DenseVector(3.3, 1.0, 6.3, 3.6) // these are row vectors
a: breeze.linalg.DenseVector[Double] = DenseVector(3.3, 1.0, 6.3, 3.6)
a.size // a is a column vector of size 4
res6: Int = 4
a(1) // the second element of a is indexed by 1 as the first element is indexed by 0
res7: Double = 1.0
val a = DenseVector[Double](5, 4, -1) // this makes a vector of Doubles from input Int
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val a = DenseVector(5.0, 4.0, -1.0) // this makes a vector of Doubles from type inference . NOTE "5.0" is needed not just "5."
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val x = DenseVector.zeros[Double](5) // this will output x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)

Transpose

(watch now 1:02 = 95-157 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

Suggested Home Work for the linear-algebraically rusty (LiAlRusty): watch again and take notes.

val A = DenseMatrix((1, 4), (6, 1), (3, 5)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1  4
6  1
3  5
A.t // transpose of A
res8: breeze.linalg.DenseMatrix[Int] =
1  6  3
4  1  5
val a = DenseVector(3.0, 4.0, 1.0)
a: breeze.linalg.DenseVector[Double] = DenseVector(3.0, 4.0, 1.0)
a.t
res9: breeze.linalg.Transpose[breeze.linalg.DenseVector[Double]] = Transpose(DenseVector(3.0, 4.0, 1.0))

Addition and Subtraction

(watch now 0:59 = 157-216 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

Suggested Home Work for LiAlRusty: watch again and take notes.

Pop Quiz:

  • what is a natural geometric interpretation of vector addition, subtraction, matric addition and subtraction?
val A = DenseMatrix((1, 4), (6, 1), (3, 5)) 
A: breeze.linalg.DenseMatrix[Int] =
1  4
6  1
3  5
val B = -A 
B: breeze.linalg.DenseMatrix[Int] =
-1  -4
-6  -1
-3  -5
A + B // should be A-A=0
res10: breeze.linalg.DenseMatrix[Int] =
0  0
0  0
0  0
A - B // should be A+A=2A
res11: breeze.linalg.DenseMatrix[Int] =
2   8
12  2
6   10
B - A // should be -A-A=-2A
res12: breeze.linalg.DenseMatrix[Int] =
-2   -8
-12  -2
-6   -10

Operators

All Tensors support a set of operators, similar to those used in Matlab or Numpy.

For HOMEWORK see: Workspace -> scalable-data-science -> xtraResources -> LinearAlgebra -> LAlgCheatSheet for a list of most of the operators and various operations.

Some of the basic ones are reproduced here, to give you an idea.

OperationBreezeMatlabNumpy
Elementwise additiona + ba + ba + b
Elementwise multiplicationa :* ba .* ba * b
Elementwise comparisona :< ba < b (gives matrix of 1/0 instead of true/false)a < b
Inplace additiona :+= 1.0a += 1a += 1
Inplace elementwise multiplicationa :*= 2.0a *= 2a *= 2
Vector dot producta dot b,a.t * bdot(a,b)dot(a,b)
Elementwise sumsum(a)sum(sum(a))a.sum()
Elementwise maxa.maxmax(a)a.max()
Elementwise argmaxargmax(a)argmax(a)a.argmax()
Ceilingceil(a)ceil(a)ceil(a)
Floorfloor(a)floor(a)floor(a)

Suggested Home Work for LiAlRusty: watch again and take notes.

Pop Quiz:

  • what is a natural geometric interpretation of scalar multiplication of a vector or a matrix and what about vector matrix multiplication?

Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications from the first interactive visual-cognitive aid at:

val A = DenseMatrix((1, 4), (6, 1), (3, 5)) 
A: breeze.linalg.DenseMatrix[Int] =
1  4
6  1
3  5
5 * A
res13: breeze.linalg.DenseMatrix[Int] =
5   20
30  5
15  25
A * 5
res14: breeze.linalg.DenseMatrix[Int] =
5   20
30  5
15  25
val A = DenseMatrix((1, 4), (6, 1), (3, 5)) 
A: breeze.linalg.DenseMatrix[Int] =
1  4
6  1
3  5
val B = DenseMatrix((3, 1), (2, 2), (1, 3)) 
B: breeze.linalg.DenseMatrix[Int] =
3  1
2  2
1  3
A *:* B // element-wise multiplication
res15: breeze.linalg.DenseMatrix[Int] =
3   4
12  2
3   15
val A = DenseMatrix((1, 4), (3, 1)) 
A: breeze.linalg.DenseMatrix[Int] =
1  4
3  1
val a = DenseVector(1, -1) // is a column vector
a: breeze.linalg.DenseVector[Int] = DenseVector(1, -1)
a.size // a is a column vector of size 2
res16: Int = 2
A * a
res17: breeze.linalg.DenseVector[Int] = DenseVector(-3, 2)

Matrix-Matrix multiplication

(watch now 1:59 = 380-499 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

val A = DenseMatrix((1,2,3),(1,1,1))
A: breeze.linalg.DenseMatrix[Int] =
1  2  3
1  1  1
val B = DenseMatrix((4, 1), (9, 2), (8, 9))
B: breeze.linalg.DenseMatrix[Int] =
4  1
9  2
8  9
A*B // 4+18+14
res18: breeze.linalg.DenseMatrix[Int] =
46  32
21  12
1*4 + 2*9 + 3*8 // checking first entry of A*B
res19: Int = 46

Identity Matrix

(watch now 0:53 = 530-583 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

val A = DenseMatrix((1,2,3),(4,5,6))
A: breeze.linalg.DenseMatrix[Int] =
1  2  3
4  5  6
DenseMatrix.eye[Int](3)
res20: breeze.linalg.DenseMatrix[Int] =
1  0  0
0  1  0
0  0  1
A * DenseMatrix.eye[Int](3)
res21: breeze.linalg.DenseMatrix[Int] =
1  2  3
4  5  6
DenseMatrix.eye[Int](2) * A
res22: breeze.linalg.DenseMatrix[Int] =
1  2  3
4  5  6

Inverse

(watch now 0:52 = 584-636 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

val D = DenseMatrix((2.0, 3.0), (4.0, 5.0))
val Dinv = inv(D)
D: breeze.linalg.DenseMatrix[Double] =
2.0  3.0
4.0  5.0
Dinv: breeze.linalg.DenseMatrix[Double] =
-2.5  1.5
2.0   -1.0
D * Dinv
res23: breeze.linalg.DenseMatrix[Double] =
1.0  0.0
0.0  1.0
Dinv * D
res24: breeze.linalg.DenseMatrix[Double] =
1.0  0.0
0.0  1.0

Eucledian distance / norm

(watch now 0:52 = 638-682 seconds):

Linear Algebra Review by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

val b = DenseVector(4, 3)
norm(b)
b: breeze.linalg.DenseVector[Int] = DenseVector(4, 3)
res25: Double = 5.0
Math.sqrt(4*4 + 3*3) // check
res26: Double = 5.0

HOMEWORK: read this! https://github.com/scalanlp/breeze/wiki/Quickstart

It is here in markdown'd via wget and pandoc for your convenience.

Scala / nlp / breeze / Quickstart

David Hall edited this page on 24 Dec 2015

Breeze is modeled on Scala, and so if you're familiar with it, you'll be familiar with Breeze. First, import the linear algebra package:

scala> import breeze.linalg._

Let's create a vector:

scala> val x = DenseVector.zeros[Double](5)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)

Here we make a column vector of zeros of type Double. And there are other ways we could create the vector - such as with a literal DenseVector(1,2,3) or with a call to fill or tabulate. The vector is "dense" because it is backed by an Array[Double], but could as well have created a SparseVector.zeros[Double](5), which would not allocate memory for zeros.

Unlike Scalala, all Vectors are column vectors. Row vectors are represented as Transpose[Vector[T]].

The vector object supports accessing and updating data elements by their index in 0 to x.length-1. Like Numpy, negative indices are supported, with the semantics that for an index i < 0 we operate on the i-th element from the end (x(i) == x(x.length + i)).

scala> x(0)
Double = 0.0

scala> x(1) = 2

scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.0, 0.0)

Breeze also supports slicing. Note that slices using a Range are much, much faster than those with an arbitrary sequence.

scala> x(3 to 4) := .5
breeze.linalg.DenseVector[Double] = DenseVector(0.5, 0.5)

scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.5, 0.5)

The slice operator constructs a read-through and write-through view of the given elements in the underlying vector. You set its values using the vectorized-set operator :=. You could as well have set it to a compatibly sized Vector.

scala> x(0 to 1) := DenseVector(.1,.2)

scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.1, 0.2, 0.0, 0.5, 0.5)

Similarly, a DenseMatrix can be created with a constructor method call, and its elements can be accessed and updated.

scala> val m = DenseMatrix.zeros[Int](5,5)
m: breeze.linalg.DenseMatrix[Int] = 
0  0  0  0  0  
0  0  0  0  0  
0  0  0  0  0  
0  0  0  0  0  
0  0  0  0  0  

The columns of m can be accessed as DenseVectors, and the rows as DenseMatrices.

scala> (m.rows, m.cols)
(Int, Int) = (5,5)

scala> m(::,1)
breeze.linalg.DenseVector[Int] = DenseVector(0, 0, 0, 0, 0)

scala>  m(4,::) := DenseVector(1,2,3,4,5).t  // transpose to match row shape
breeze.linalg.DenseMatrix[Int] = 1  2  3  4  5 

scala> m
breeze.linalg.DenseMatrix[Int] = 
0  0  0  0  0  
0  0  0  0  0  
0  0  0  0  0  
0  0  0  0  0  
1  2  3  4  5   

Assignments with incompatible cardinality or a larger numeric type won't compile.

scala> m := x
<console>:13: error: could not find implicit value for parameter op: breeze.linalg.operators.BinaryUpdateOp[breeze.linalg.DenseMatrix[Int],breeze.linalg.DenseVector[Double],breeze.linalg.operators.OpSet]
              m := x
                ^

Assignments with incompatible size will throw an exception:

scala> m := DenseMatrix.zeros[Int](3,3)
java.lang.IllegalArgumentException: requirement failed: Matrices must have same number of row

Sub-matrices can be sliced and updated, and literal matrices can be specified using a simple tuple-based syntax. Unlike Scalala, only range slices are supported, and only the columns (or rows for a transposed matrix) can have a Range step size different from 1.

scala> m(0 to 1, 0 to 1) := DenseMatrix((3,1),(-1,-2)) 
breeze.linalg.DenseMatrix[Int] = 
3   1   
-1  -2  

scala> m
breeze.linalg.DenseMatrix[Int] = 
3   1   0  0  0  
-1  -2  0  0  0  
0   0   0  0  0  
0   0   0  0  0  
1   2   3  4  5  

Broadcasting

Sometimes we want to apply an operation to every row or column of a matrix, as a unit. For instance, you might want to compute the mean of each row, or add a vector to every column. Adapting a matrix so that operations can be applied column-wise or row-wise is called broadcasting. Languages like R and numpy automatically and implicitly do broadcasting, meaning they won't stop you if you accidentally add a matrix and a vector. In Breeze, you have to signal your intent using the broadcasting operator *. The * is meant to evoke "foreach" visually. Here are some examples:

scala> import breeze.stats.mean

scala> val dm = DenseMatrix((1.0,2.0,3.0),
                            (4.0,5.0,6.0))

scala> val res = dm(::, *) + DenseVector(3.0, 4.0)
breeze.linalg.DenseMatrix[Double] =
4.0  5.0  6.0
8.0  9.0  10.0

scala> res(::, *) := DenseVector(3.0, 4.0)

scala> res
breeze.linalg.DenseMatrix[Double] =
3.0  3.0  3.0
4.0  4.0  4.0

scala> mean(dm(*, ::))
breeze.linalg.DenseVector[Double] = DenseVector(2.0, 5.0)

breeze.stats.distributions

Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.

scala> import breeze.stats.distributions._

scala> val poi = new Poisson(3.0);
poi: breeze.stats.distributions.Poisson = <function1>

scala> val s = poi.sample(5);
s: IndexedSeq[Int] = Vector(5, 4, 5, 7, 4)

scala> s map { poi.probabilityOf(_) }
IndexedSeq[Double] = Vector(0.10081881344492458, 0.16803135574154085, 0.10081881344492458, 0.02160403145248382, 0.16803135574154085)

scala> val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = breeze.stats.distributions.Rand$$anon$11@1b52e04

scala> breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.9960000000000067,2.9669509509509533,1000)

scala> (poi.mean,poi.variance)
(Double, Double) = (3.0,3.0)

NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.

scala> val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)

scala> expo.rate
Double = 0.5

A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.

scala> expo.probability(0, log(2) * expo.rate)
Double = 0.5

scala> expo.probability(0.0, 1.5)
Double = 0.950212931632136

This means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well

scala> 1 - exp(-3.0)
Double = 0.950212931632136


scala> val samples = expo.sample(2).sorted;
samples: IndexedSeq[Double] = Vector(1.1891135726280517, 2.325607782657507)

scala> expo.probability(samples(0), samples(1));
Double = 0.08316481553047272

scala> breeze.stats.meanAndVariance(expo.samples.take(10000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.029351863973081,4.163267835527843,10000)

scala> (1 / expo.rate, 1 / (expo.rate * expo.rate))
(Double, Double) = (2.0,4.0)

breeze.optimize

TODO: document breeze.optimize.minimize, recommend that instead.

Breeze's optimization package includes several convex optimization routines and a simple linear program solver. Convex optimization routines typically take a DiffFunction[T], which is a Function1 extended to have a gradientAt method, which returns the gradient at a particular point. Most routines will require a breeze.linalg-enabled type: something like a Vector or a Counter.

Here's a simple DiffFunction: a parabola along each vector's coordinate.

scala> import breeze.optimize._

scala>  val f = new DiffFunction[DenseVector[Double]] {
     |               def calculate(x: DenseVector[Double]) = {
     |                 (norm((x - 3d) :^ 2d,1d),(x * 2d) - 6d);
     |               }
     |             }
f: java.lang.Object with breeze.optimize.DiffFunction[breeze.linalg.DenseVector[Double]] = $anon$1@617746b2

Note that this function takes its minimum when all values are 3. (It's just a parabola along each coordinate.)

scala> f.valueAt(DenseVector(3,3,3))
Double = 0.0

scala> f.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(0.0, -6.0, -4.0)

scala>  f.calculate(DenseVector(0,0))
(Double, breeze.linalg.DenseVector[Double]) = (18.0,DenseVector(-6.0, -6.0))

You can also use approximate derivatives, if your function is easy enough to compute:

scala> def g(x: DenseVector[Double]) = (x - 3.0):^ 2.0 sum

scala> g(DenseVector(0.,0.,0.))
Double = 27.0

scala> val diffg = new ApproximateGradientFunction(g)

scala> diffg.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(1.000000082740371E-5, -5.999990000127297, -3.999990000025377)

Ok, now let's optimize f. The easiest routine to use is just LBFGS, which is a quasi-Newton method that works well for most problems.

scala> val lbfgs = new LBFGS[DenseVector[Double]](maxIter=100, m=3) // m is the memory. anywhere between 3 and 7 is fine. The larger m, the more memory is needed.

scala> val optimum = lbfgs.minimize(f,DenseVector(0,0,0))
optimum: breeze.linalg.DenseVector[Double] = DenseVector(2.9999999999999973, 2.9999999999999973, 2.9999999999999973)

scala> f(optimum)
Double = 2.129924444096732E-29

That's pretty close to 0! You can also use a configurable optimizer, using FirstOrderMinimizer.OptParams. It takes several parameters:

case class OptParams(batchSize:Int = 512,
                     regularization: Double = 1.0,
                     alpha: Double = 0.5,
                     maxIterations:Int = -1,
                     useL1: Boolean = false,
                     tolerance:Double = 1E-4,
                     useStochastic: Boolean= false) {
  // ...
}

batchSize applies to BatchDiffFunctions, which support using small minibatches of a dataset. regularization integrates L2 or L1 (depending on useL1) regularization with constant lambda. alpha controls the initial stepsize for algorithms that need it. maxIterations is the maximum number of gradient steps to be taken (or -1 for until convergence). tolerance controls the sensitivity of the convergence check. Finally, useStochastic determines whether or not batch functions should be optimized using a stochastic gradient algorithm (using small batches), or using LBFGS (using the entire dataset).

OptParams can be controlled using breeze.config.Configuration, which we described earlier.

breeze.optimize.linear

We provide a DSL for solving linear programs, using Apache's Simplex Solver as the backend. This package isn't industrial strength yet by any means, but it's good for simple problems. The DSL is pretty simple:

import breeze.optimize.linear._
val lp = new LinearProgram()
import lp._
val x0 = Real()
val x1 = Real()
val x2 = Real()

val lpp =  ( (x0 +  x1 * 2 + x2 * 3 )
    subjectTo ( x0 * -1 + x1 + x2 <= 20)
    subjectTo ( x0 - x1 * 3 + x2 <= 30)
    subjectTo ( x0 <= 40 )
)

val result = maximize( lpp)

assert( norm(result.result - DenseVector(40.0,17.5,42.5), 2) < 1E-4)

We also have specialized routines for bipartite matching (KuhnMunkres and CompetitiveLinking) and flow problems.

Breeze-Viz

**This API is highly experimental. It may change greatly. **

Breeze continues most of the functionality of Scalala's plotting facilities, though the API is somewhat different (in particular, more object oriented.) These methods are documented in scaladoc for the traits in the breeze.plot package object. First, let's plot some lines and save the image to file. All the actual plotting work is done by the excellent JFreeChart library.

import breeze.linalg._
import breeze.plot._

val f = Figure()
val p = f.subplot(0)
val x = linspace(0.0,1.0)
p += plot(x, x :^ 2.0)
p += plot(x, x :^ 3.0, '.')
p.xlabel = "x axis"
p.ylabel = "y axis"
f.saveas("lines.png") // save current figure as a .png, eps and pdf also supported

two functions

Then we'll add a new subplot and plot a histogram of 100,000 normally distributed random numbers into 100 buckets.

val p2 = f.subplot(2,1,1)
val g = breeze.stats.distributions.Gaussian(0,1)
p2 += hist(g.sample(100000),100)
p2.title = "A normal distribution"
f.saveas("subplots.png")

two plots

Breeze also supports the Matlab-like "image" command, here imaging a random matrix.

val f2 = Figure()
f2.subplot(0) += image(DenseMatrix.rand(200,200))
f2.saveas("image.png")

two plots

Where to go next?

After reading this quickstart, you can go to other wiki pages, especially Linear Algebra Cheat-Sheet and Data Structures.

Watch this and take notes if you have not experienced big-O and big-Omega and big-Theta notation.

ScaDaMaLe Course site and book

Let us visit an interactive visual cognitive tool for the basics ideas in linear regression:

The following video is a very concise and thorough treatment of linear regression for those who have taken the 200-level linear algebra. Others can fully understand it with some effort and revisiting.

Ridge regression has a Bayesian interpretation where the weights have a zero-mean Gaussian prior. See 7.5 in Murphy's Machine Learning: A Probabilistic Perspective for details.

Please take notes in mark-down if you want.

For latex math within markdown you can do the following for in-line maths: \(\mathbf{A}_{i,j} \in \mathbb{R}^1\). And to write maths in display mode do the following:

\[\mathbf{A} \in \mathbb{R}^{m \times d} \]

You will need to write such notes for your final project presentation!

MillonSongs Ridge Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

(watch later 7:47):

Linear Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

Covers the training, test and validation and grid search... ridger regression...

Take your own notes if you like.

Please take notes if you want to. Most of the above should be review to most of you.

Getting our hands dirty with Darren Wilkinson's blog on regression

Let's follow the exposition in:

You need to scroll down the fitst link embedded in iframe below to get to the section on Analysis of quantitative data with Descriptive statistics and Linear regression sub-sections (you can skip the earlier part that shows you how to run everything locally in spark-shell - try this on your own later).

Let's do this live now...

import breeze.stats.distributions._
def x = Gaussian(1.0,2.0).sample(10000)
val xRdd = sc.parallelize(x)
import breeze.stats.distributions._
x: IndexedSeq[Double]
xRdd: org.apache.spark.rdd.RDD[Double] = ParallelCollectionRDD[752] at parallelize at command-2972105651606193:3
println(xRdd.mean)
println(xRdd.sampleVariance)
1.0205021258164175
3.9857177279359335
val xStats = xRdd.stats
xStats.mean
xStats.sampleVariance
xStats.sum
xStats: org.apache.spark.util.StatCounter = (count: 10000, mean: 1.020502, stdev: 1.996326, max: 8.089040, min: -6.775216)
res1: Double = 10205.021258164175
val x2 = Gaussian(0.0,1.0).sample(10000) // 10,000 Gaussian samples with mean 0.0 and std dev 1.0
val xx = x zip x2 // creating tuples { (x,x2)_1, ... , (x,x2)_10000}
val lp = xx map {p => 2.0*p._1 + 1.0*p._2 + 1.5} // { lp_i := (2.0*x + 1.0 * x2 + 1.5)_i, i=1,...,10000 }
val eps = Gaussian(0.0,1.0).sample(10000) // standrad normal errors
val y = (lp zip eps) map (p => p._1 + p._2)
val yx = (y zip xx) map (p => (p._1, p._2._1, p._2._2))
x2: IndexedSeq[Double] = Vector(-0.39294234164655323, 0.8555415681712383, 0.5047531440104407, -1.7447287479723848, 0.24074263385944478, -2.5060458696190544, -0.3625957248008947, -1.590842317493378, 0.6957126527559235, 0.4443439308434807, 1.0693145537223456, -1.2513129429033423, -0.35885347259074074, -0.4025589013089939, -0.5152842487123, 1.132054878042222, -0.5455845613656002, 0.047570983504133746, -0.49567564166101635, 1.0696261814411365, 0.05742293214978136, -0.34714185801134384, 1.8582008120255742, -0.6693454894738627, -0.24016699291536123, -0.12433010673208071, 0.862434789491131, -1.1316281054134634, -0.10575269488815854, 1.7745918273414025, -1.3061660094876109, -0.06572880649671899, 0.3873544789319996, -0.12210660688891804, -1.165943599251054, -0.36104975279667534, 0.42769260065418174, 2.5268542442818207, -0.4635835110880271, 0.5605742432439522, -0.18424533745017588, -0.6547651770934155, -0.3093631599212501, -1.6992194371388365, 1.0251808317424254, -0.49932778415411466, 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(2.044815209793586,2.2435222794640977), (2.4410440661546104,0.2995966594358739), (-2.349135440873335,-0.5291073022086537), (0.6790935771846645,-0.5571189095542917), (3.4139257418589426,0.11349223107935802), (-0.8626084002064136,1.1185669488897558), (-0.37391747446349344,-0.4086114125519275), (0.8215544444978548,-0.25112950363112646), (3.8618090128750024,-1.0608970223945158), (1.6800857686675532,0.17518246249620026), (1.0074005694996364,0.06651951321430995), (3.5318329860970725,-0.33034634898439175), (1.7950417717994815,0.6105745637327348), (-2.0336070695788977,0.5290626446720255), (-1.7054184477763195,2.018433101361855), (4.645023899050502,-0.9984024008645774), (1.3906629527780199,0.3260796702342513), (2.4740987497226086,-0.3250349484487747), (0.07847588428334362,-1.369769865042985), (2.475868347546835,0.9921216716815834), (0.6272709897847293,-0.3083526711709522), (-0.6858175920919738,1.0187493429512395), (0.45900544246416697,-0.24782056078650502), (-0.5745269203716783,-0.7712163035518746), (1.3701280631677617,-3.5604118223788594), (0.0948731510554367,0.32612291143440386), (-1.6852745529042497,0.4441259968243954), (1.6709633112857247,-1.4734210872528921), (2.3048068787912106,1.4195884324887544), (3.7027681434110313,-1.4331865020277441), (3.9197357289539516,0.5398675146254653), (3.4591727918555657,0.15162238606384537), (0.18141111286742795,0.43294396379213923), (1.1418538021916915,1.1004558318130622), (1.8458371307314194,-1.6225249551448202), (0.9244775944028731,0.890121110279592), (1.0864146130345944,-0.6294534363395626), (-0.3509024113650585,0.7003661935454487), (3.33728122430862,-0.1295402687875722), (0.2483092092697341,2.044822501564002), (3.9964209499931576,-0.8395262566314947), (5.041222565400819,-0.07787176492505386), (3.027508004726709,0.654084260252699), (-1.4004317476058188,-1.108634225084861), (1.9893187143877324,0.5376683045798573), (-1.510601790533327,-0.5998211693601727), (3.2336996361552006,1.0847220613718276), (1.3904890738297762,-1.2062551172960978), (-0.9985183459863718,-0.07704375636574601), (-0.42290918516396814,-0.22710371366501256), (0.557145424969743,0.6448982623891638), (0.6299424035592602,-1.3123779835257174), (1.2441200282402665,1.5005705164947913), (0.1416628957035737,-0.1530590588270949), (1.658439228273565,0.2968215409652417), (3.487027565153457,-0.17609073023938399), (0.057747803745601956,2.0723474186637874), (1.8158892220290406,0.18760101905530316), (0.47388630687051536,-1.1629786052155737), (-2.8455031410603127,-0.04644466219506257), (2.568676770051918,1.102684460944185), (-0.8084331842961674,-0.8597310131420156), (2.7731501328334534,1.378930732154014), (-2.2022646763305564,-0.6932188375333033), (-2.5747511745197507,-1.427569318035048), (0.045385950152493915,-0.4316528243072038), (0.48651128153317813,-2.438723481595101), (0.17211394730373364,0.7217274318503094), (2.4559329674426524,1.0155995742344546), (-1.949177472936245,0.052126413971228996), (3.151379032883542,-0.48445743390746254), (-2.008398402592464,0.4148805797424512), (2.1051344549387263,0.6133447153641186), (1.3539082426943376,0.8753910134615039), (-0.7773573700545062,-1.068076214407843), (3.136090265926976,-0.434556342004716), (0.8659619601166967,3.1269586338444797), (-2.756659224225406,-1.0898133189009078), (0.40970541978049146,-1.6792860321548917), (0.5481531611505823,1.1100130243189885), (1.4013179773665998,0.19785059877372296), (2.2133971545815765,1.1381515852738975), (-0.9491287644096225,1.6951931898940376), (0.5209829893925308,0.4849524249938774), (-2.9358313706560333,1.0375329570798115), (-2.1863280999191628,2.3962090984380273), (1.8973103339115684,0.10582200453150249), (-0.9438088917696854,0.12261906596813583), (-3.8161788015537486,-0.15685142307025152), (4.598243989073854,0.39785792430853506), (0.14157990229434658,-2.063728946132821), (0.7712012756880778,0.006718652661392625), (1.657954492689108,0.8811082917215027), (-1.9831854962565685,1.031451541304585), (-0.3480671912553863,1.03387374103542), (2.8738286816216583,-0.21407256559986743), (1.863514328094054,-0.5812016751411385), (-0.9428403558267164,-0.7583304518772646), (1.754785995282709,-1.7509940657503635), (0.013682513757415804,-0.6818646320163245), (-0.5248686453084859,0.03344337152195518), (6.071914898933014,0.07593147801064193), (-1.958247927497752,0.4280940512841111), (4.708222732187144,1.7068789507250506), (2.945611634833644,0.41457568923249366), (-2.873611006165206,0.7191845635928295), (-0.6308622794536811,-1.0212751896145356), (1.7810775590894088,0.5752383319361283), (1.3389808014307991,1.0226769514361524), (1.810527500961158,0.4821255564311748), (-0.6433026011303964,0.6925122151119577), (2.5056823298048334,-0.49731955088091157), (-2.920132259436649,-0.2964847770300842), (2.037990236118738,-1.7889384504694956), (1.4786574552223295,-1.7308205970757709), (-3.2626435921253814,-0.8978527372381635), (3.202111558516957,1.610161749388444), (3.0775529358503873,-2.3346966778146676), (-0.004858289779320346,-0.14005235243669392), (-0.2356611818482255,-0.2638362330334334), (1.0363597159426476,3.033939148221663), (0.5951880796739928,-0.4723921431007955), (-1.385157144701036,-1.9818612938644127), (3.168627913505496,-1.200180759439612), (1.7659099443795578,-0.1958021131712377), (-2.2099743662353757,1.8451977668782862), (-2.030174316332396,-1.5854513074199188), (1.7759112730502544,-0.6861915752981226), (-1.4724017242345253,-0.8314228441600172), (-3.090438319803951,-0.5338604341531643), (-1.002042900848683,1.781652090388434), (2.6306174515890004,-0.9076613846556846), (-1.908448678609222,-0.6071641904585964), (1.4736263162987941,0.25535155484588434), (0.7736703824491263,1.5909788064424866), (1.7498656979196905,1.2231795468357605), (0.9366265907877988,-1.5369768230738219), (-0.09536435134844679,0.9778022047574955), (0.8177132188563772,-0.11568918189852785), (-3.4121939733497646,-0.43046323075751886), (3.4314196035854083,0.4802026430220832), (1.2692425660828197,2.1631891668981025), (0.9537947988635598,0.4690583697476523), (1.9696982561485377,1.0674375424842637), (3.9301829681795217,0.9208080500687618), (-0.4472709746146637,0.6843434412137375), (-2.253286341939513,-1.6794382154599943), (0.026135514256517056,1.0870090254688949), (2.4177497341772787,1.9248747606955043), (-0.835370698709712,0.6934671953307943), (4.135177415806311,0.627881336513211), (-1.3712161103055798,0.3744662154421754), (1.410688895640302,1.763784024349061), (-1.942154218131955,-0.8493808371900399), (0.38330995987308936,-0.567609188833336), (-1.0334804970971412,-0.2201046369367792), (-1.832625134688632,-1.6873491715309796), (2.5310364388620794,0.2371190560666023), (3.452713263233051,1.075251848522166), (-0.6837517757525475,1.4368687592287923), (-0.2942544269504741,0.8762404262364163), (1.4256959022956015,-0.07711565494502963), (2.7974786021190114,0.17698197228232837), (1.323363871505369,-0.3600776896107076), (2.7466288331322652,1.150261456652663), (-1.413730211824194,-0.5002...
val rddLR = sc.parallelize(yx)
rddLR.take(5)
rddLR: org.apache.spark.rdd.RDD[(Double, Double, Double)] = ParallelCollectionRDD[756] at parallelize at command-2972105651606197:1
res2: Array[(Double, Double, Double)] = Array((-0.6846711453351213,-1.5289467818444833,-0.39294234164655323), (5.497385056408215,1.699419230239045,0.8555415681712383), (3.192289974143172,0.5678191719510146,0.5047531440104407), (9.62629002945858,4.451573214358624,-1.7447287479723848), (2.3798191201086443,-0.1120835302184493,0.24074263385944478))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show
dfLR.show(5)
+--------------------+--------------------+--------------------+
|                   y|                  x1|                  x2|
+--------------------+--------------------+--------------------+
| -0.6846711453351213| -1.5289467818444833|-0.39294234164655323|
|   5.497385056408215|   1.699419230239045|  0.8555415681712383|
|   3.192289974143172|  0.5678191719510146|  0.5047531440104407|
|    9.62629002945858|   4.451573214358624| -1.7447287479723848|
|  2.3798191201086443| -0.1120835302184493| 0.24074263385944478|
|  2.8219504009739422|   2.593362084700087| -2.5060458696190544|
|   3.612693960033294|  0.5534949593776934| -0.3625957248008947|
|-0.43223384405620746|   0.610162799267687|  -1.590842317493378|
|  2.6768474073335398|  0.6079959554264716|  0.6957126527559235|
|  7.1521815633527535|  3.5570523071163964|  0.4443439308434807|
| -0.9227166193659311| -1.6847794191419774|  1.0693145537223456|
|  -2.468553589310911| -0.9209298511507433| -1.2513129429033423|
|   3.898027197661611|  1.7696585529469604|-0.35885347259074074|
|   6.732593845464607|   2.513719756856524| -0.4025589013089939|
| 0.43416427898655346| -0.5101432900051885|    -0.5152842487123|
|   4.214978206657414|  0.9531168056474683|   1.132054878042222|
|  1.4665466181948976|-0.18160386853639432| -0.5455845613656002|
| -3.7361996916879003| -1.6720534639191835|0.047570983504133746|
|  0.9602040797406759| -0.1128951628965178|-0.49567564166101635|
|  12.928871086510929|   5.053202646224175|  1.0696261814411365|
+--------------------+--------------------+--------------------+
only showing top 20 rows

+-------------------+-------------------+--------------------+
|                  y|                 x1|                  x2|
+-------------------+-------------------+--------------------+
|-0.6846711453351213|-1.5289467818444833|-0.39294234164655323|
|  5.497385056408215|  1.699419230239045|  0.8555415681712383|
|  3.192289974143172| 0.5678191719510146|  0.5047531440104407|
|   9.62629002945858|  4.451573214358624| -1.7447287479723848|
| 2.3798191201086443|-0.1120835302184493| 0.24074263385944478|
+-------------------+-------------------+--------------------+
only showing top 5 rows

dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
// importing for regression
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._

val lm = new LinearRegression
lm.explainParams
lm.getStandardization
lm.setStandardization(false)
lm.getStandardization
lm.explainParams
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
lm: org.apache.spark.ml.regression.LinearRegression = linReg_6bc65ac2a454
res5: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true, current: false)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
// Transform data frame to required format
val dflr = (dfLR map {row => (row.getDouble(0), 
           Vectors.dense(row.getDouble(1),row.getDouble(2)))}).
           toDF("label","features")
dflr.show(5)
+-------------------+--------------------+
|              label|            features|
+-------------------+--------------------+
|-0.6846711453351213|[-1.5289467818444...|
|  5.497385056408215|[1.69941923023904...|
|  3.192289974143172|[0.56781917195101...|
|   9.62629002945858|[4.45157321435862...|
| 2.3798191201086443|[-0.1120835302184...|
+-------------------+--------------------+
only showing top 5 rows

dflr: org.apache.spark.sql.DataFrame = [label: double, features: vector]
// Fit model
val fit = lm.fit(dflr)
fit.intercept
fit: org.apache.spark.ml.regression.LinearRegressionModel = LinearRegressionModel: uid=linReg_6bc65ac2a454, numFeatures=2
res8: Double = 1.4864717388481623
fit.coefficients
res9: org.apache.spark.ml.linalg.Vector = [2.0076393809346107,1.0168498373858426]
val summ = fit.summary
summ: org.apache.spark.ml.regression.LinearRegressionTrainingSummary = org.apache.spark.ml.regression.LinearRegressionTrainingSummary@3ff7b1a0
summ.r2
res10: Double = 0.9439028933334824
summ.rootMeanSquaredError
res11: Double = 0.9917259206430321
summ.coefficientStandardErrors
res12: Array[Double] = Array(0.005062858724537394, 0.0098766852367649, 0.01122127338207313)
summ.pValues
res13: Array[Double] = Array(0.0, 0.0, 0.0)
summ.tValues
res14: Array[Double] = Array(396.5426432312811, 102.95456552576245, 132.46907799456326)
summ.predictions.show(5)
+-------------------+--------------------+-------------------+
|              label|            features|         prediction|
+-------------------+--------------------+-------------------+
|-0.6846711453351213|[-1.5289467818444...|-1.9826653879413711|
|  5.497385056408215|[1.69941923023904...|  5.768250014665403|
|  3.192289974143172|[0.56781917195101...| 3.1397060221137103|
|   9.62629002945858|[4.45157321435862...|  8.649498287450083|
| 2.3798191201086443|[-0.1120835302184...| 1.5062475377192448|
+-------------------+--------------------+-------------------+
only showing top 5 rows
summ.residuals.show(5)
+--------------------+
|           residuals|
+--------------------+
|  1.2979942426062498|
| -0.2708649582571878|
|0.052583952029461756|
|  0.9767917420084977|
|  0.8735715823893995|
+--------------------+
only showing top 5 rows

This gives you more on doing generalised linear modelling in Scala. But let's go back to out pipeline using the power-plant data.

Exercise: Writing your own Spark program for the least squares fit

Your task is to use the syntactic sugar below to write your own linear regression function using reduce and broadcast operations

How would you write your own Spark program to find the least squares fit on the following 1000 data points in RDD rddLR, where the variable y is the response variable, and X1 and X2 are independent variables?

More precisely, find \(w_1, w_2\), such that,

\(\sum_{i=1}^{10} (w_1 X1_i + w_2 X2_i - y_i)^2\) is minimized.

Report \(w_1\), \(w_2\), and the Root Mean Square Error and submit code in Spark. Analyze the resulting algorithm in terms of all-to-all, one-to-all, and all-to-one communication patterns.

rddLR.count
res39: Long = 10000
rddLR.take(10)
res33: Array[(Double, Double, Double)] = Array((-0.6846711453351213,-1.5289467818444833,-0.39294234164655323), (5.497385056408215,1.699419230239045,0.8555415681712383), (3.192289974143172,0.5678191719510146,0.5047531440104407), (9.62629002945858,4.451573214358624,-1.7447287479723848), (2.3798191201086443,-0.1120835302184493,0.24074263385944478), (2.8219504009739422,2.593362084700087,-2.5060458696190544), (3.612693960033294,0.5534949593776934,-0.3625957248008947), (-0.43223384405620746,0.610162799267687,-1.590842317493378), (2.6768474073335398,0.6079959554264716,0.6957126527559235), (7.1521815633527535,3.5570523071163964,0.4443439308434807))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show(10)
+--------------------+-------------------+--------------------+
|                   y|                 x1|                  x2|
+--------------------+-------------------+--------------------+
| -0.6846711453351213|-1.5289467818444833|-0.39294234164655323|
|   5.497385056408215|  1.699419230239045|  0.8555415681712383|
|   3.192289974143172| 0.5678191719510146|  0.5047531440104407|
|    9.62629002945858|  4.451573214358624| -1.7447287479723848|
|  2.3798191201086443|-0.1120835302184493| 0.24074263385944478|
|  2.8219504009739422|  2.593362084700087| -2.5060458696190544|
|   3.612693960033294| 0.5534949593776934| -0.3625957248008947|
|-0.43223384405620746|  0.610162799267687|  -1.590842317493378|
|  2.6768474073335398| 0.6079959554264716|  0.6957126527559235|
|  7.1521815633527535| 3.5570523071163964|  0.4443439308434807|
+--------------------+-------------------+--------------------+
only showing top 10 rows

dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
rddLR.getNumPartitions
res47: Int = 8
rddLR.map( yx1x2 => (yx1x2._2, yx1x2._3, yx1x2._1) ).take(10)
res45: Array[(Double, Double, Double)] = Array((-1.5289467818444833,-0.39294234164655323,-0.6846711453351213), (1.699419230239045,0.8555415681712383,5.497385056408215), (0.5678191719510146,0.5047531440104407,3.192289974143172), (4.451573214358624,-1.7447287479723848,9.62629002945858), (-0.1120835302184493,0.24074263385944478,2.3798191201086443), (2.593362084700087,-2.5060458696190544,2.8219504009739422), (0.5534949593776934,-0.3625957248008947,3.612693960033294), (0.610162799267687,-1.590842317493378,-0.43223384405620746), (0.6079959554264716,0.6957126527559235,2.6768474073335398), (3.5570523071163964,0.4443439308434807,7.1521815633527535))
import breeze.linalg.DenseVector
    val pts = rddLR.map( yx1x2 => DenseVector(yx1x2._2, yx1x2._3, yx1x2._1) ).cache
import breeze.linalg.DenseVector
pts: org.apache.spark.rdd.RDD[breeze.linalg.DenseVector[Double]] = MapPartitionsRDD[810] at map at command-2744653148556786:3

Now all we need to do is one-to-all and all-to-one broadcast of the gradient...

val w = DenseVector(0.0, 0.0, 0.0)
val w_bc = sc.broadcast(w)
val step = 0.1
val max_iter = 100

/*
YouTry: Fix the expressions for grad_w0, grad_w1 and grad_w2 to have w_bc.value be the same as that from ml lib's fit.coefficients
*/    
for (i <- 1 to max_iter) {
        val grad_w0 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*1.0).reduce(_+_)
        val grad_w1 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(0)).reduce(_+_)
        val grad_w2 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(1)).reduce(_+_)
        w_bc.value(0) = w_bc.value(0) - step*grad_w0
        w_bc.value(1) = w_bc.value(1) - step*grad_w1
        w_bc.value(2) = w_bc.value(2) - step*grad_w2
}
w: breeze.linalg.DenseVector[Double] = DenseVector(713529.4020469891, 2282475.3330477937, 208861.12768314985)
w_bc: org.apache.spark.broadcast.Broadcast[breeze.linalg.DenseVector[Double]] = Broadcast(2899)
step: Double = 0.1
max_iter: Int = 100
w_bc.value
res69: breeze.linalg.DenseVector[Double] = DenseVector(713529.4020469891, 2282475.3330477937, 208861.12768314985)
fit.intercept
res70: Double = 1.4864717388481623
fit.coefficients
res71: org.apache.spark.ml.linalg.Vector = [2.0076393809346107,1.0168498373858426]

Computing each of the gradients requires an all-to-one communication (due to the .reduce(_+_)). There are two of these per iteration. Broadcasting the updated w_bc requires one to all communication.

ScaDaMaLe Course site and book

We need this more detailed deep dive

HOMEWORK:

This is a transcript of Ameet's lecture in the video above:

HOMEWORK: Make sure you are understanding everything he is trying to convey!

Recall from week 1's lectures that we are weaving over Ameet's course here to get to advanced applications "in a hurry"...

In this segment, we'll begin our discussion of distributed machine learning principles related to computation and storage. We'll use linear regression as a running example to illustrate these ideas. As we previously discussed, the size of datasets is rapidly growing. And this has led to scalability issues for many standard machine learning methods. For instance, consider the least squares regression model. The fact that a closed-form solution exists is an appealing property of this method. But what happens when we try to solve this closed-form solution at scale? In this segment, we'll focus on computational issues associated with linear regression. Though I should note that these same ideas apply in the context of ridge regression, as it has a very similar computational profile. So let's figure out the time and space complexity of solving for this closed-form solution. We'll start with time complexity, considering arithmetic operations as the basic units of computation when discussing big O complexity. Looking at the expression for w, if we perform each of the required operations separately, we see that computing X transpose X takes O of nd squared time, and inverting this resulting matrix takes O of d cubed time. Since matrix multiplication is an associative operation, we can multiply X transpose and y in O of nd time to get some intermediate d dimensional vector, and then perform the multiplication between the inverse matrix and this intermediate d dimensional vector in O of d squared time. Hence, in summary, the two computational bottlenecks in this process involve computing X transpose X, and subsequently computing its inverse. There are other methods to solve this equation that can be faster in practice, but they are only faster in terms of constants, and therefore they all have the same overall time complexity. In summary, we say that computing the closed-form solution for linear regression takes O of nd squared plus d cubed time. Now let's consider the space complexity. And recall that our basic unit of storage here is the storage required to store a single float, which is typically 8 bytes. In order to compute w, we must first store the data matrix, which requires O of nd floats. Additionally, we must compute X transpose X and its inverse. In order to solve for w, each of these matrices are d by d and thus require O of d squared floats. These are the two bottlenecks storage-wise. And thus our space complexity is O of nd plus d squared. So now that we've considered the time and space complexity required to solve for the closed-form solution, let's consider what happens as our data grows large. The first situation we'll consider is one where n, or the number of observations, is large, while d, or the number of features, is relatively small. Specifically, we'll assume that we're in a setting where d is small enough such that O of d cubed operate computation and O of d squared storage is feasible on a single machine. In this scenario, the terms in our big O complexity involving n are the ones that dominate, and thus storing X and computing X transpose X are the bottlenecks. It turns out that in this scenario, we're well suited for a distributed computation. First, we can store the data points or rows of X across several machines, thus reducing the storage burden. Second, we can compute X transpose X in parallel across the machines by treating this multiplication as a sum of outer products. To understand this alternative interpretation of matrix multiplication in terms of outer products, let's first recall our typical definition of matrix multiplication. We usually think about each entry of the output matrix being computed via an inner product between rows and columns of the input matrices. So, for instance, in the example on the slide, to compute the top left entry, we compute the inner product between the first row of the left input matrix and the first column of the right input matrix. Similarly, to compute the top right entry, we compute the inner product between the first row of the left input matrix and the second column of the right input matrix. We perform additional inner products to compute the two remaining entries of the output matrix. There is, however, an alternative interpretation of matrix multiplication as the sum of outer products between corresponding rows and columns of the input matrices. Let's look at the same example from the last slide to get a better sense of what this means. First consider the first column of the left input matrix and the first row of the right input matrix. We can compute their outer product with the result being the 2 by 2 matrix on the bottom of the slide. Next, we can consider the second column of the left input matrix and the second row of the right input matrix, and again compute their outer product, resulting in another 2 by 2 matrix. We can repeat this process a third time to generate a third outer product or 2 by 2 matrix. The sum of these outer products matches the result we obtained in the previous slide using the traditional definition of matrix multiplication. And more generally, taking a sum of outer products of corresponding rows and columns of the input matrices, always returns the desired matrix multiplication result. Now we can use this new interpretation of matrix multiplication to our benefit when distributing the computation of X transpose X for linear regression. Let's first represent X visually by its rows or data points. Then we can express this matrix multiplication as a sum of outer products where each outer product involves only a single row of X or a single data point. Let's see how we can use this insight to effectively distribute our computation. Consider a toy example where we have a cluster of three workers and a data set with six data points. We can distribute the storage of our six data points across the three workers so that each worker is storing two data points. Now we can express matrix multiplication as a simple MapReduce operation. In the map step, we take each point and compute its outer product with itself. And in the subsequent reduce step, we simply sum over all of these outer products. We can then solve for the final linear regression model locally, which includes computing the inverse of this resulting matrix. Now let's look at the storage and computation involved at each step. In the first step, we're not doing any computation, but we need to store the input data, which requires O of nd storage. This is a bottleneck in our setting since n is large. However, the storage can be distributed over several machines. Next, during the map step, we perform an outer product for each data point. Each outer product takes O of d squared time, and we have to compute n of these outer products. This is the computational bottleneck in our setting, but again, it is distributed across multiple workers. In terms of storage, we must store the outer products computed on each machine. Note that although we may be computing several outer products per machine, we can keep a running sum of these outer products, so the local storage required for each machine is O of d squared. Finally, in the reduce step, we must take the sum of these outer products, though the computational bottleneck is, in fact, inverting the resulting matrix, which is cubic nd. However, we're assuming that d is small enough for this computation to be feasible on a single machine. Similarly, the O of d squared storage required to store X transpose X and its inverse is also feasible on a single machine by assumption. This entire process can be concisely summarized via the following Spark code snippet. In this code, train data is an RDD of rows of X. In the map step, we compute an outer product for each row. And in the reduce step, we sum these outer products and invert the resulting matrix. In the final reduce step, we can also perform the remaining steps required to obtain our final regression model.

This is a transcript of Ameet's lecture in the video above:

Homework: Make sure you are understanding everything he is trying to convey!

In this segment, we'll continue our discussion of distributed machine learning principles related to computation and storage. We'll focus on the problem when D, the number of features, grows large. In the previous segment, we discussed the big N small D setting. In this setting, we can naturally use a distributed computing environment to solve for the linear regression closed form solution. To do this, we store our data across multiple machines and we compute X transpose X as a sum of outer products. This strategy can be written as a simple MapReduce operation, expressed very concisely in Spark. Now, let's consider what happens when D grows large. As before, storing X and computing X transpose X are bottlenecks. However, storing and operating on X transpose X is now also a bottleneck. And we can no longer use our previous strategy. So let's see what goes wrong. Here's what our strategy looks like in the small D setting with data stored across workers, outer products computed in the map step, and sum of these outer products performed in the reduced step. However, we can no longer perform D cubed operations locally or store D squared floats locally in our new setting. This issue leads to a more general rule of thumb, which is that when N and D are large, we need the computation and storage complexity to be at most linear in N and D. So how do we devise methods that are linear in space and time complexity? One idea is to exploit sparsity. Sparse data is quite prevalent in practice. Some data is inherently sparse, such as rating information and collaborative filtering problems or social networking or other grafted. Additionally, we often generate sparse features during a process of feature extraction, such as when we represent text documents via a bag-of-words features or when we convert categorical features into numerical representations. Accounting for sparsity can lead to orders of magnitudes of savings in terms of storage and computation. A second idea is to make a late and sparsity assumption, whereby we make the assumption that our high dimensional data can in fact be represented in a more succinct fashion, either exactly or approximately. For example, we can make a low rank modeling assumption where we might assume that our data matrix can in fact be represented by the product of two skinny matrices, where the skinny dimension R is much smaller than either N or D. Exploiting this assumption can also yield significant computational and storage gigs. A third option is to use different algorithms. For instance, instead of learning a linear regression model via the closed form solution, we could alternatively use gradient descent. Gradient descent is an iterative algorithm that requires layer computation and storage at each iteration thus making it attractive in the big N and big D setting. So let's see how gradient descent stacks up with a closed form solution in our toy example on a cluster with three machines. As before, we can store the data across the worker machines. Now in the map step, we require O of ND computation, and this computation is distributed across workers. And we also require O of D storage locally. In the reduced step, we require O of D local computation as well as O of D local storage. Moreover, unlike the closed form case, we need to repeat this process several times since gradient descent is an iterative algorithm. At this point, I haven't really told you how these question marks work. And in the next segment, we'll talk about what actually is going on with gradient decent.

SUMMARY: Access rates fall sharply with distance.
  • roughly 50 x gap between reading from memory and reading from either disk or the network.

We must take this communication hierarchy into consideration when developing parallel and distributed algorithms.

Focusing on strategies to reduce communication costs.

  • access rates fall sharply with distance.
  • so this communication hierarchy needs to be accounted for when developing parallel and distributed algorithms.

Lessons:

  • parallelism makes our computation faster

  • but network communication slows us down

  • BINGO: perform parallel and in-memory computation.

  • Persisting in memory is a particularly attractive option when working with iterative algorithms that read the same data multiple times, as is the case in gradient descent.

  • Several machine learning algorithms are iterative!

  • Limits of multi-core scaling (powerful multicore machine with several CPUs, and a huge amount of RAM).

    • advantageous:
      • sidestep any network communication when working with a single multicore machine
      • can indeed handle fairly large data sets, and they're an attractive option in many settings.
    • disadvantages:
      • can be quite expensive (due to specialized hardware),
      • not as widely accessible as commodity computing nodes.
      • this approach does have scalability limitations, as we'll eventually hit a wall when the data grows large enough! This is not the case for a distributed environment (like the AWS EC2 cloud under the hood here).

Simple strategies for algorithms in a distributed setting: to reduce network communication, simply keep large objects local

  • In the big n, small d case for linear regression
    • we can solve the problem via a closed form solution.
    • And this requires us to communicate \(O(d)^2\) intermediate data.
    • the largest object in this example is our initial data, which we store in a distributed fashion and never communicate! This is a data parallel setting.
  • In the big n, big d case:
    • for linear regression.
      • we use gradient descent to iteratively train our model and are again in a data parallel setting.
      • At each iteration we communicate the current parameter vector \(w_i\) and the required \(O(d)\) communication is feasible even for fairly large d.
  • In the small n, small d case:
    • for ridge regression
      • we can communicate the small data to all of the workers.
      • this is an example of a model parallel setting where we can train the model for each hyper-parameter in parallel.
  • Linear regression with big n and huge d is an example of both data and model parallelism.

HOMEWORK: Watch the video and find out why Linear regression with big n and huge d is an example of both data and model parallelism.

In this setting, since our data is large, we must still store it across multiple machines. We can still use gradient descent, or stochastic variants of gradient descent to train our model, but we may not want to communicate the entire d dimensional parameter vector at each iteration, when we have 10s, or hundreds of millions of features. In this setting we often rely on sparsity to reduce the communication. So far we discussed how we can reduce communication by keeping large data local.

Simple strategies for algorithms in a distributed setting: compute more and communicate less per iteration

HOMEWORK: watch the video and understand why it is important at each iteration of an iterative algorithm to compute more and communicate less.

Recall from week 1's lecture that the ideal mathematical preparation to fully digest this material requires a set of self-tutorials from Reza Zadeh's course in Distributed Algorithms and Optimization from Stanford:

This is a minimal pre-requisite for designing new algorithms or improving exixting ones.

Notes for these lectures will be provided for self-study.

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

Why are we going through the basic distributed linear algebra Data Types?

This is to get you to be able to directly use them in a project or to roll your own algorithms.

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

Local vector in Scala

A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine.

MLlib supports two types of local vectors:

  • dense and
  • sparse.

A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values.

For example, a vector (1.0, 0.0, 3.0) can be represented:

  • in dense format as [1.0, 0.0, 3.0] or
  • in sparse format as (3, [0, 2], [1.0, 3.0]), where 3 is the size of the vector.

The base class of local vectors is Vector, and we provide two implementations: DenseVector and SparseVector. We recommend using the factory methods implemented in Vectors to create local vectors. Refer to the Vector Scala docs and Vectors Scala docs for details on the API.

import org.apache.spark.mllib.linalg.{Vector, Vectors}

// Create a dense vector (1.0, 0.0, 3.0).
val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)
import org.apache.spark.mllib.linalg.{Vector, Vectors}
dv: org.apache.spark.mllib.linalg.Vector = [1.0,0.0,3.0]
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))
sv1: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.
val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
sv2: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])

Note: Scala imports scala.collection.immutable.Vector by default, so you have to import org.apache.spark.mllib.linalg.Vector explicitly to use MLlib’s Vector.

python: MLlib recognizes the following types as dense vectors:

  • NumPy’s array
  • Python’s list, e.g., [1, 2, 3]

and the following as sparse vectors:

We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented in Vectors to create sparse vectors.

Refer to the Vectors Python docs for more details on the API.

import numpy as np
import scipy.sparse as sps
from pyspark.mllib.linalg import Vectors

# Use a NumPy array as a dense vector.
dv1 = np.array([1.0, 0.0, 3.0])
# Use a Python list as a dense vector.
dv2 = [1.0, 0.0, 3.0]
# Create a SparseVector.
sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])
# Use a single-column SciPy csc_matrix as a sparse vector.
sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))
print (dv1)
print (dv2)
print (sv1)
print (sv2)
[1. 0. 3.]
[1.0, 0.0, 3.0]
(3,[0,2],[1.0,3.0])
  (0, 0)	1.0
  (2, 0)	3.0

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

Labeled point in Scala

A labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms.

We use a double to store a label, so we can use labeled points in both regression and classification.

For binary classification, a label should be either 0 (negative) or 1 (positive). For multiclass classification, labels should be class indices starting from zero: 0, 1, 2, ....

A labeled point is represented by the case class LabeledPoint.

Refer to the LabeledPoint Scala docs for details on the API.

//import first
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
// Create a labeled point with a "positive" label and a dense feature vector.
val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))
pos: org.apache.spark.mllib.regression.LabeledPoint = (1.0,[1.0,0.0,3.0])
// Create a labeled point with a "negative" label and a sparse feature vector.
val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
neg: org.apache.spark.mllib.regression.LabeledPoint = (0.0,(3,[0,2],[1.0,3.0]))

Sparse data in Scala

It is very common in practice to have sparse training data. MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR. It is a text format in which each line represents a labeled sparse feature vector using the following format:

label index1:value1 index2:value2 ...

where the indices are one-based and in ascending order. After loading, the feature indices are converted to zero-based.

MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.

Refer to the MLUtils Scala docs for details on the API.

import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD

//val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") // from prog guide but no such data here - can wget from github 
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD

Load MNIST training and test datasets

Our datasets are vectors of pixels representing images of handwritten digits. For example:

Image of a digit Image of all 10 digits

display(dbutils.fs.ls("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt"))
path name size
dbfs:/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt mnist-digits-train.txt 6.9430283e7
val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[3661] at map at MLUtils.scala:88
examples.take(1)
res1: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))

Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).

display(examples.toDF) // covert to DataFrame and display for convenient db visualization

The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) below, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):

type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]

Here

  • type: 0 says we hve a sparse vector.
  • size: 780 says the vector has 780 indices in total
  • these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
  • indices: [152,153,155,...,682,683] are the indices from the [0,1,...,779] possible indices with non-zero values
    • a value is an integer encoding the gray-level at the pixel index
  • values: [3, 18, 18,18,126,...,132,16] are the actual gray level values, for example:
    • at pixed index 152 the gray-level value is 3,
    • at index 153 the gray-level value is 18,
    • ..., and finally at
    • at index 683 the gray-level value is 18

We could also use the following method as done in notebook 016_* already.

val training = spark.read.format("libsvm")
                    .option("numFeatures", "780")
                    .load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
display(training)


Labeled point in Python

A labeled point is represented by LabeledPoint.

Refer to the LabeledPoint Python docs for more details on the API.

# import first
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))

Sparse data in Python

MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.

Refer to the MLUtils Python docs for more details on the API.

from pyspark.mllib.util import MLUtils

# examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") #from prog guide but no such data here - can wget from github 
examples = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples.take(1)
res5: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

Local Matrix in Scala

A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports:

  • dense matrices, whose entry values are stored in a single double array in column-major order, and
  • sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order.

For example, the following dense matrix: \[ \begin{pmatrix} 1.0 & 2.0 \\ 3.0 & 4.0 \\ 5.0 & 6.0 \end{pmatrix} \] is stored in a one-dimensional array [1.0, 3.0, 5.0, 2.0, 4.0, 6.0] with the matrix size (3, 2).

The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.

Refer to the Matrix Scala docs and Matrices Scala docs for details on the API.

Int.MaxValue // note the largest value an index can take
res0: Int = 2147483647
import org.apache.spark.mllib.linalg.{Matrix, Matrices}

// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
dm: org.apache.spark.mllib.linalg.Matrix =
1.0  2.0
3.0  4.0
5.0  6.0

Next, let us create the following sparse local matrix: \[ \begin{pmatrix} 9.0 & 0.0 \\ 0.0 & 8.0 \\ 0.0 & 6.0 \end{pmatrix} \]

// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
sm: org.apache.spark.mllib.linalg.Matrix =
3 x 2 CSCMatrix
(0,0) 9.0
(2,1) 6.0
(1,1) 8.0

Local Matrix in Python

The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.

Refer to the Matrix Python docs and Matrices Python docs for more details on the API.

from pyspark.mllib.linalg import Matrix, Matrices

# Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
dm2
# Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
sm

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

Distributed matrix in Scala

A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs.

It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a global shuffle, which is quite expensive.

Three types of distributed matrices have been implemented so far.

  1. The basic type is called RowMatrix.
  • A RowMatrix is a row-oriented distributed matrix without meaningful row indices, e.g., a collection of feature vectors. It is backed by an RDD of its rows, where each row is a local vector.
  • We assume that the number of columns is not huge for a RowMatrix so that a single local vector can be reasonably communicated to the driver and can also be stored / operated on using a single node.
  • An IndexedRowMatrix is similar to a RowMatrix but with row indices, which can be used for identifying rows and executing joins.
  • A CoordinateMatrix is a distributed matrix stored in coordinate list (COO) format, backed by an RDD of its entries.

Note

The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors.

Remark: there is a huge difference in the orders of magnitude between the maximum size of local versus distributed matrices!

print(Long.MaxValue.toDouble, Int.MaxValue.toDouble, Long.MaxValue.toDouble / Int.MaxValue.toDouble) // index ranges and ratio for local and distributed matrices

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

RowMatrix in Scala

A RowMatrix is a row-oriented distributed matrix without meaningful row indices, backed by an RDD of its rows, where each row is a local vector. Since each row is represented by a local vector, the number of columns is limited by the integer range but it should be much smaller in practice.

A RowMatrix can be created from an RDD[Vector] instance. Then we can compute its column summary statistics and decompositions.

Refer to the RowMatrix Scala docs for details on the API.

import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
val rows: RDD[Vector] = sc.parallelize(Array(Vectors.dense(12.0, -51.0, 4.0), Vectors.dense(6.0, 167.0, -68.0), Vectors.dense(-4.0, 24.0, -41.0))) // an RDD of local vectors
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector] = ParallelCollectionRDD[3670] at parallelize at command-2972105651606776:1
// Create a RowMatrix from an RDD[Vector].
val mat: RowMatrix = new RowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@75c66624
mat.rows.collect
res0: Array[org.apache.spark.mllib.linalg.Vector] = Array([12.0,-51.0,4.0], [6.0,167.0,-68.0], [-4.0,24.0,-41.0])
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 3
n: Long = 3
// QR decomposition
val qrResult = mat.tallSkinnyQR(true)
qrResult: org.apache.spark.mllib.linalg.QRDecomposition[org.apache.spark.mllib.linalg.distributed.RowMatrix,org.apache.spark.mllib.linalg.Matrix] =
QRDecomposition(org.apache.spark.mllib.linalg.distributed.RowMatrix@34f5da4f,14.0  21.0                 -14.0
0.0   -174.99999999999997  70.00000000000001
0.0   0.0                  -35.000000000000014  )
qrResult.R
res1: org.apache.spark.mllib.linalg.Matrix =
14.0  21.0                 -14.0
0.0   -174.99999999999997  70.00000000000001
0.0   0.0                  -35.000000000000014


RowMatrix in Python

A RowMatrix can be created from an RDD of vectors.

Refer to the RowMatrix Python docs for more details on the API.

from pyspark.mllib.linalg.distributed import RowMatrix

# Create an RDD of vectors.
rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])

# Create a RowMatrix from an RDD of vectors.
mat = RowMatrix(rows)

# Get its size.
m = mat.numRows()  # 4
n = mat.numCols()  # 3
print (m,'x',n)

# Get the rows as an RDD of vectors again.
rowsRDD = mat.rows
4 x 3

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

IndexedRowMatrix in Scala

An IndexedRowMatrix is similar to a RowMatrix but with meaningful row indices. It is backed by an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.

An IndexedRowMatrix can be created from an RDD[IndexedRow] instance, where IndexedRow is a wrapper over (Long, Vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.

Refer to the IndexedRowMatrix Scala docs for details on the API.

import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
Vector(12.0, -51.0, 4.0) // note Vector is a scala collection
res0: scala.collection.immutable.Vector[Double] = Vector(12.0, -51.0, 4.0)
Vectors.dense(12.0, -51.0, 4.0) // while this is a mllib.linalg.Vector
res1: org.apache.spark.mllib.linalg.Vector = [12.0,-51.0,4.0]
val rows: RDD[IndexedRow] = sc.parallelize(Array(IndexedRow(2, Vectors.dense(1,3)), IndexedRow(4, Vectors.dense(4,5)))) // an RDD of indexed rows
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.IndexedRow] = ParallelCollectionRDD[3685] at parallelize at command-2972105651607186:1
// Create an IndexedRowMatrix from an RDD[IndexedRow].
val mat: IndexedRowMatrix = new IndexedRowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@300e1e99
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 5
n: Long = 2
// Drop its row indices.
val rowMat: RowMatrix = mat.toRowMatrix()
rowMat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@425fc4a2
rowMat.rows.collect()
res2: Array[org.apache.spark.mllib.linalg.Vector] = Array([1.0,3.0], [4.0,5.0])

IndexedRowMatrix in Python

An IndexedRowMatrix can be created from an RDD of IndexedRows, where IndexedRow is a wrapper over (long, vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.

Refer to the IndexedRowMatrix Python docs for more details on the API.

from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix

# Create an RDD of indexed rows.
#   - This can be done explicitly with the IndexedRow class:
indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
                              IndexedRow(1, [4, 5, 6]),
                              IndexedRow(2, [7, 8, 9]),
                              IndexedRow(3, [10, 11, 12])])

#   - or by using (long, vector) tuples:
indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
                              (2, [7, 8, 9]), (3, [10, 11, 12])])

# Create an IndexedRowMatrix from an RDD of IndexedRows.
mat = IndexedRowMatrix(indexedRows)

# Get its size.
m = mat.numRows()  # 4
n = mat.numCols()  # 3
print (m,n)

# Get the rows as an RDD of IndexedRows.
rowsRDD = mat.rows

# Convert to a RowMatrix by dropping the row indices.
rowMat = mat.toRowMatrix()

# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()

# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
4 3

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

CoordinateMatrix in Scala

A CoordinateMatrix is a distributed matrix backed by an RDD of its entries. Each entry is a tuple of (i: Long, j: Long, value: Double), where i is the row index, j is the column index, and value is the entry value. A CoordinateMatrix should be used only when both dimensions of the matrix are huge and the matrix is very sparse.

A CoordinateMatrix can be created from an RDD[MatrixEntry] instance, where MatrixEntry is a wrapper over (Long, Long, Double). A CoordinateMatrix can be converted to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix. Other computations for CoordinateMatrix are not currently supported.

Refer to the CoordinateMatrix Scala docs for details on the API.

import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3712] at parallelize at command-2972105651606627:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val mat: CoordinateMatrix = new CoordinateMatrix(entries)
mat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@56954954
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 7
n: Long = 2
// Convert it to an IndexRowMatrix whose rows are sparse vectors.
val indexedRowMatrix = mat.toIndexedRowMatrix()
indexedRowMatrix: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@45570b82
indexedRowMatrix.rows.collect()
res0: Array[org.apache.spark.mllib.linalg.distributed.IndexedRow] = Array(IndexedRow(0,(2,[0],[1.2])), IndexedRow(1,(2,[0],[2.1])), IndexedRow(6,(2,[1],[3.7])))

CoordinateMatrix in Scala

A CoordinateMatrix can be created from an RDD of MatrixEntry entries, where MatrixEntry is a wrapper over (long, long, float). A CoordinateMatrix can be converted to a RowMatrix by calling toRowMatrix, or to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix.

Refer to the CoordinateMatrix Python docs for more details on the API.

from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry

# Create an RDD of coordinate entries.
#   - This can be done explicitly with the MatrixEntry class:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])

#   - or using (long, long, float) tuples:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])

# Create an CoordinateMatrix from an RDD of MatrixEntries.
mat = CoordinateMatrix(entries)

# Get its size.
m = mat.numRows()  # 3
n = mat.numCols()  # 2
print (m,n)

# Get the entries as an RDD of MatrixEntries.
entriesRDD = mat.entries

# Convert to a RowMatrix.
rowMat = mat.toRowMatrix()

# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()

# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
3 2

ScaDaMaLe Course site and book

This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.

Overview

Data Types - MLlib Programming Guide

MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.

BlockMatrix in Scala

A BlockMatrix is a distributed matrix backed by an RDD of MatrixBlocks, where a MatrixBlock is a tuple of ((Int, Int), Matrix), where the (Int, Int) is the index of the block, and Matrix is the sub-matrix at the given index with size rowsPerBlock x colsPerBlock. BlockMatrix supports methods such as add and multiply with another BlockMatrix. BlockMatrix also has a helper function validate which can be used to check whether the BlockMatrix is set up properly.

A BlockMatrix can be most easily created from an IndexedRowMatrix or CoordinateMatrix by calling toBlockMatrix. toBlockMatrix creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through toBlockMatrix(rowsPerBlock, colsPerBlock).

Refer to the BlockMatrix Scala docs for details on the API.

//import org.apache.spark.mllib.linalg.{Matrix, Matrices}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3746] at parallelize at command-2972105651607062:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val coordMat: CoordinateMatrix = new CoordinateMatrix(entries)
coordMat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@161a1942
// Transform the CoordinateMatrix to a BlockMatrix
val matA: BlockMatrix = coordMat.toBlockMatrix().cache()
matA: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@1ddf85ce
// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid.
// Nothing happens if it is valid.
matA.validate()
// Calculate A^T A.
val ata = matA.transpose.multiply(matA)
ata: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@18c0eeca
ata.blocks.collect()
res1: Array[((Int, Int), org.apache.spark.mllib.linalg.Matrix)] =
Array(((0,0),5.85  0.0
0.0   13.690000000000001  ))
ata.toLocalMatrix()
res2: org.apache.spark.mllib.linalg.Matrix =
5.85  0.0
0.0   13.690000000000001

BlockMatrix in Scala

A BlockMatrix can be created from an RDD of sub-matrix blocks, where a sub-matrix block is a ((blockRowIndex, blockColIndex), sub-matrix) tuple.

Refer to the BlockMatrix Python docs for more details on the API.

from pyspark.mllib.linalg import Matrices
from pyspark.mllib.linalg.distributed import BlockMatrix

# Create an RDD of sub-matrix blocks.
blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
                         ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])

# Create a BlockMatrix from an RDD of sub-matrix blocks.
mat = BlockMatrix(blocks, 3, 2)

# Get its size.
m = mat.numRows() # 6
n = mat.numCols() # 2
print (m,n)

# Get the blocks as an RDD of sub-matrix blocks.
blocksRDD = mat.blocks

# Convert to a LocalMatrix.
localMat = mat.toLocalMatrix()

# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()

# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
6 2

ScaDaMaLe Course site and book

Power Plant ML Pipeline Application

This is an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.

Table of Contents

  • Step 1: Business Understanding
  • Step 2: Load Your Data
  • Step 3: Explore Your Data
  • Step 4: Visualize Your Data
  • Step 5: Data Preparation
  • Step 6: Data Modeling
  • Step 7: Tuning and Evaluation
  • Step 8: Deployment

We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.

More information about Peaker or Peaking Power Plants can be found on Wikipedia https://en.wikipedia.org/wiki/Peakingpowerplant

Given this business problem, we need to translate it to a Machine Learning task. The ML task is regression since the label (or target) we are trying to predict is numeric.

The example data is provided by UCI at UCI Machine Learning Repository Combined Cycle Power Plant Data Set

You can read the background on the UCI page, but in summary we have collected a number of readings from sensors at a Gas Fired Power Plant

(also called a Peaker Plant) and now we want to use those sensor readings to predict how much power the plant will generate.

More information about Machine Learning with Spark can be found in the Spark MLLib Programming Guide

Please note this example only works with Spark version 1.4 or higher



To Rerun Steps 1-4 done in the notebook at:

  • Workspace -> PATH_TO -> 009_PowerPlantPipeline_01ETLEDA]

just run the following command as shown in the cell below:

%run "/PATH_TO/009_PowerPlantPipeline_01ETLEDA"
  • Note: If you already evaluated the %run ... command above then:

    • first delete the cell by pressing on x on the top-right corner of the cell and
    • revaluate the run command above.
"../009_PowerPlantPipeline_01ETLEDA" 


Now we will do the following Steps:

Step 5: Data Preparation,

Step 6: Modeling, and

Step 7: Tuning and Evaluation

We will do Step 8: Deployment later after we get introduced to SparkStreaming.

Step 5: Data Preparation

The next step is to prepare the data. Since all of this data is numeric and consistent, this is a simple task for us today.

We will need to convert the predictor features from columns to Feature Vectors using the org.apache.spark.ml.feature.VectorAssembler

The VectorAssembler will be the first step in building our ML pipeline.

res2: Int = 301
//Let's quickly recall the schema and make sure our table is here now
table("power_plant_table").printSchema
root
 |-- AT: double (nullable = true)
 |-- V: double (nullable = true)
 |-- AP: double (nullable = true)
 |-- RH: double (nullable = true)
 |-- PE: double (nullable = true)
path name size
dbfs:/databricks-datasets/power-plant/data/Sheet1.tsv Sheet1.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet2.tsv Sheet2.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet3.tsv Sheet3.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet4.tsv Sheet4.tsv 308693.0
dbfs:/databricks-datasets/power-plant/data/Sheet5.tsv Sheet5.tsv 308693.0
powerPlantRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/power-plant/data/Sheet1.tsv MapPartitionsRDD[1] at textFile at command-1267216879634554:1
powerPlantDF // make sure we have the DataFrame too
res82: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
AT	V	AP	RH	PE
14.96	41.76	1024.07	73.17	463.26
25.18	62.96	1020.04	59.08	444.37
5.11	39.4	1012.16	92.14	488.56
20.86	57.32	1010.24	76.64	446.48
powerPlantDF: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
root
 |-- AT: double (nullable = true)
 |-- V: double (nullable = true)
 |-- AP: double (nullable = true)
 |-- RH: double (nullable = true)
 |-- PE: double (nullable = true)
import org.apache.spark.ml.feature.VectorAssembler

// make a DataFrame called dataset from the table
val dataset = sqlContext.table("power_plant_table") 

val vectorizer =  new VectorAssembler()
                      .setInputCols(Array("AT", "V", "AP", "RH"))
                      .setOutputCol("features")
import org.apache.spark.ml.feature.VectorAssembler
dataset: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
vectorizer: org.apache.spark.ml.feature.VectorAssembler = VectorAssembler: uid=vecAssembler_ea4ed428c7e4, handleInvalid=error, numInputCols=4
res9: Long = 9568
+-----+-----+-------+-----+------+
|   AT|    V|     AP|   RH|    PE|
+-----+-----+-------+-----+------+
|14.96|41.76|1024.07|73.17|463.26|
|25.18|62.96|1020.04|59.08|444.37|
| 5.11| 39.4|1012.16|92.14|488.56|
|20.86|57.32|1010.24|76.64|446.48|
|10.82| 37.5|1009.23|96.62| 473.9|
|26.27|59.44|1012.23|58.77|443.67|
|15.89|43.96|1014.02|75.24|467.35|
| 9.48|44.71|1019.12|66.43|478.42|
|14.64| 45.0|1021.78|41.25|475.98|
|11.74|43.56|1015.14|70.72| 477.5|
+-----+-----+-------+-----+------+
only showing top 10 rows
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68
res12: Long = 9568
+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
+--------+--------------------+-----------+
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name                                                        |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|power_plant_predictions                                     |default |null       |MANAGED  |false      |
|sentimentlex_csv                                            |default |null       |EXTERNAL |false      |
|simple_range                                                |default |null       |MANAGED  |false      |
|social_media_usage                                          |default |null       |MANAGED  |false      |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null       |MANAGED  |false      |
+------------------------------------------------------------+--------+-----------+---------+-----------+
+-------+---------------------+-------------------------+
|name   |description          |locationUri              |
+-------+---------------------+-------------------------+
|default|Default Hive database|dbfs:/user/hive/warehouse|
+-------+---------------------+-------------------------+

Step 6: Data Modeling

Now let's model our data to predict what the power output will be given a set of sensor readings

Our first model will be based on simple linear regression since we saw some linear patterns in our data based on the scatter plots during the exploration stage.

+--------+--------------------+-----------+
|database|           tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...|      false|
| default|    sentimentlex_csv|      false|
| default|        simple_range|      false|
| default|  social_media_usage|      false|
| default|social_media_usag...|      false|
|        |   power_plant_table|       true|
+--------+--------------------+-----------+
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68
col_name data_type comment
AT double null
V double null
AP double null
RH double null
PE double null
summary AT V AP RH PE
count 9568 9568 9568 9568 9568
mean 19.65123118729102 54.30580372073601 1013.2590781772603 73.30897784280926 454.3650094063554
stddev 7.4524732296110825 12.707892998326784 5.938783705811581 14.600268756728964 17.066994999803402
min 1.81 25.36 992.89 25.56 420.26
max 37.11 81.56 1033.3 100.16 495.76
Temperature Power
14.96 463.26
25.18 444.37
5.11 488.56
20.86 446.48
10.82 473.9
26.27 443.67
15.89 467.35
9.48 478.42
14.64 475.98
11.74 477.5
17.99 453.02
20.14 453.99
24.34 440.29
25.71 451.28
26.19 433.99
21.42 462.19
18.21 467.54
11.04 477.2
14.45 459.85
13.97 464.3
17.76 468.27
5.41 495.24
7.76 483.8
27.23 443.61
27.36 436.06
27.47 443.25
14.6 464.16
7.91 475.52
5.81 484.41
30.53 437.89
23.87 445.11
26.09 438.86
29.27 440.98
27.38 436.65
24.81 444.26
12.75 465.86
24.66 444.37
16.38 450.69
13.91 469.02
23.18 448.86
22.47 447.14
13.39 469.18
9.28 482.8
11.82 476.7
10.27 474.99
22.92 444.22
16.0 461.33
21.22 448.06
13.46 474.6
9.39 473.05
31.07 432.06
12.82 467.41
32.57 430.12
8.11 473.62
13.92 471.81
23.04 442.99
27.31 442.77
5.91 491.49
25.26 447.46
27.97 446.11
26.08 442.44
29.01 446.22
12.18 471.49
13.76 463.5
25.5 440.01
28.26 441.03
21.39 452.68
7.26 474.91
10.54 478.77
27.71 434.2
23.11 437.91
7.51 477.61
26.46 431.65
29.34 430.57
10.32 481.09
22.74 445.56
13.48 475.74
25.52 435.12
21.58 446.15
27.66 436.64
26.96 436.69
12.29 468.75
15.86 466.6
13.87 465.48
24.09 441.34
20.45 441.83
15.07 464.7
32.72 437.99
18.23 459.12
35.56 429.69
18.36 459.8
26.35 433.63
25.92 442.84
8.01 485.13
19.63 459.12
20.02 445.31
10.08 480.8
27.23 432.55
23.37 443.86
18.74 449.77
14.81 470.71
23.1 452.17
10.72 478.29
29.46 428.54
8.1 478.27
27.29 439.58
17.1 457.32
11.49 475.51
23.69 439.66
13.51 471.99
9.64 479.81
25.65 434.78
21.59 446.58
27.98 437.76
18.8 459.36
18.28 462.28
13.55 464.33
22.99 444.36
23.94 438.64
13.74 470.49
21.3 455.13
27.54 450.22
24.81 440.43
4.97 482.98
15.22 460.44
23.88 444.97
33.01 433.94
25.98 439.73
28.18 434.48
21.67 442.33
17.67 457.67
21.37 454.66
28.69 432.21
16.61 457.66
27.91 435.21
20.97 448.22
10.8 475.51
20.61 446.53
25.45 441.3
30.16 433.54
4.99 472.52
10.51 474.77
33.79 435.1
21.34 450.74
23.4 442.7
32.21 426.56
14.26 463.71
27.71 447.06
21.95 452.27
25.76 445.78
23.68 438.65
8.28 480.15
23.44 447.19
25.32 443.04
3.94 488.81
17.3 455.75
18.2 455.86
21.43 457.68
11.16 479.11
30.38 432.84
23.36 448.37
21.69 447.06
23.62 443.53
21.87 445.21
29.25 441.7
20.03 450.93
18.14 451.44
24.23 441.29
18.11 458.85
6.57 481.46
12.56 467.19
13.4 461.54
27.1 439.08
14.28 467.22
16.29 468.8
31.24 426.93
10.57 474.65
13.8 468.97
25.3 433.97
18.06 450.53
25.42 444.51
15.07 469.03
11.75 466.56
20.23 457.57
27.31 440.13
28.57 433.24
17.9 452.55
23.83 443.29
27.92 431.76
17.34 454.97
17.94 456.7
6.4 486.03
11.78 472.79
20.28 452.03
21.04 443.41
25.11 441.93
30.28 432.64
8.14 480.25
16.86 466.68
6.25 494.39
22.35 454.72
17.98 448.71
21.19 469.76
20.94 450.71
24.23 444.01
19.18 453.2
20.88 450.87
23.67 441.73
14.12 465.09
25.23 447.28
6.54 491.16
20.08 450.98
24.67 446.3
27.82 436.48
15.55 460.84
24.26 442.56
13.45 467.3
11.06 479.13
24.91 441.15
22.39 445.52
11.95 475.4
14.85 469.3
10.11 463.57
23.67 445.32
16.14 461.03
15.11 466.74
24.14 444.04
30.08 434.01
14.77 465.23
27.6 440.6
13.89 466.74
26.85 433.48
12.41 473.59
13.08 474.81
18.93 454.75
20.5 452.94
30.72 435.83
7.55 482.19
13.49 466.66
15.62 462.59
24.8 447.82
10.03 462.73
22.43 447.98
14.95 462.72
24.78 442.42
23.2 444.69
14.01 466.7
19.4 453.84
30.15 436.92
6.91 486.37
29.04 440.43
26.02 446.82
5.89 484.91
26.52 437.76
28.53 438.91
16.59 464.19
22.95 442.19
23.96 446.86
17.48 457.15
6.69 482.57
10.25 476.03
28.87 428.89
12.04 472.7
22.58 445.6
15.12 464.78
25.48 440.42
27.87 428.41
23.72 438.5
25.0 438.28
8.42 476.29
22.46 448.46
29.92 438.99
11.68 471.8
14.04 471.81
19.86 449.82
25.99 442.14
23.42 441.46
10.6 477.62
20.97 446.76
14.14 472.52
8.56 471.58
24.86 440.85
29.0 431.37
27.59 437.33
10.45 469.22
8.51 471.11
29.82 439.17
22.56 445.33
11.38 473.71
20.25 452.66
22.42 440.99
14.85 467.42
25.62 444.14
19.85 457.17
13.67 467.87
24.39 442.04
16.07 471.36
11.6 460.7
31.38 431.33
29.91 432.6
19.67 447.61
27.18 443.87
21.39 446.87
10.45 465.74
19.46 447.86
23.55 447.65
23.35 437.87
9.26 483.51
10.3 479.65
20.94 455.16
23.13 431.91
12.77 470.68
28.29 429.28
19.13 450.81
24.44 437.73
20.32 460.21
20.54 442.86
12.16 482.99
28.09 440.0
9.25 478.48
21.75 455.28
23.7 436.94
16.22 461.06
24.75 438.28
10.48 472.61
29.53 426.85
12.59 470.18
23.5 455.38
29.01 428.32
9.75 480.35
19.55 455.56
21.05 447.66
24.72 443.06
21.19 452.43
10.77 477.81
28.68 431.66
29.87 431.8
22.99 446.67
24.66 445.26
32.63 425.72
31.38 430.58
23.87 439.86
25.6 441.11
27.62 434.72
30.1 434.01
12.19 475.64
13.11 460.44
28.29 436.4
13.45 461.03
10.98 479.08
26.48 435.76
13.07 460.14
25.56 442.2
22.68 447.69
28.86 431.15
22.7 445.0
27.89 431.59
13.78 467.22
28.14 445.33
11.8 470.57
10.71 473.77
24.54 447.67
11.54 474.29
29.47 437.14
29.24 432.56
14.51 459.14
22.91 446.19
27.02 428.1
13.49 468.46
30.24 435.02
23.19 445.52
17.73 462.69
18.62 455.75
12.85 463.74
32.33 439.79
25.09 443.26
29.45 432.04
16.91 465.86
14.09 465.6
10.73 469.43
23.2 440.75
8.21 481.32
9.3 479.87
16.97 458.59
23.69 438.62
25.13 445.59
9.86 481.87
11.33 475.01
26.95 436.54
15.0 456.63
20.76 451.69
14.29 463.04
19.74 446.1
26.68 438.67
14.24 466.88
21.98 444.6
22.75 440.26
8.34 483.92
11.8 475.19
8.81 479.24
30.05 434.92
16.01 454.16
21.75 447.58
13.94 467.9
29.25 426.29
22.33 447.02
16.43 455.85
11.5 476.46
23.53 437.48
21.86 452.77
6.17 491.54
30.19 438.41
11.67 476.1
15.34 464.58
11.5 467.74
25.53 442.12
21.27 453.34
28.37 425.29
28.39 449.63
13.78 462.88
14.6 464.67
5.1 489.96
7.0 482.38
26.3 437.95
30.56 429.2
21.09 453.34
28.21 442.47
15.84 462.6
10.03 478.79
20.37 456.11
21.19 450.33
33.73 434.83
29.87 433.43
19.62 456.02
9.93 485.23
9.43 473.57
14.24 469.94
12.97 452.07
7.6 475.32
8.39 480.69
25.41 444.01
18.43 465.17
10.31 480.61
11.29 476.04
22.61 441.76
29.34 428.24
18.87 444.77
13.21 463.1
11.3 470.5
29.23 431.0
27.76 430.68
29.26 436.42
25.72 452.33
23.43 440.16
25.6 435.75
22.3 449.74
27.91 430.73
30.35 432.75
21.78 446.79
7.19 486.35
20.88 453.18
24.19 458.31
9.98 480.26
23.47 448.65
26.35 458.41
29.89 435.39
19.29 450.21
17.48 459.59
25.21 445.84
23.3 441.08
15.42 467.33
21.44 444.19
29.45 432.96
29.69 438.09
15.52 467.9
11.47 475.72
9.77 477.51
22.6 435.13
8.24 477.9
17.01 457.26
19.64 467.53
10.61 465.15
12.04 474.28
29.19 444.49
21.75 452.84
23.66 435.38
27.05 433.57
29.63 435.27
18.2 468.49
32.22 433.07
26.88 430.63
29.05 440.74
8.9 474.49
18.93 449.74
27.49 436.73
23.1 434.58
11.22 473.93
31.97 435.99
13.32 466.83
31.68 427.22
23.69 444.07
13.83 469.57
18.32 459.89
11.05 479.59
22.03 440.92
10.23 480.87
23.92 441.9
29.38 430.2
17.35 465.16
9.81 471.32
4.97 485.43
5.15 495.35
21.54 449.12
7.94 480.53
18.77 457.07
21.69 443.67
10.07 477.52
13.83 472.95
10.45 472.54
11.56 469.17
23.64 435.21
10.48 477.78
13.09 475.89
10.67 483.9
12.57 476.2
14.45 462.16
14.22 471.05
6.97 484.71
20.61 446.34
14.67 469.02
29.06 432.12
14.38 467.28
32.51 429.66
11.79 469.49
8.65 485.87
9.75 481.95
9.11 479.03
23.39 434.5
14.3 464.9
17.49 452.71
31.1 429.74
19.77 457.09
28.61 446.77
13.52 460.76
13.52 471.95
17.57 453.29
28.18 441.61
14.29 464.73
18.12 464.68
31.27 430.59
26.24 438.01
7.44 479.08
29.78 436.39
23.37 447.07
10.62 479.91
5.84 489.05
14.51 463.17
11.31 471.26
11.25 480.49
9.18 473.78
19.82 455.5
24.77 446.27
9.66 482.2
21.96 452.48
18.59 464.48
24.75 438.1
24.37 445.6
29.6 442.43
25.32 436.67
16.15 466.56
15.74 457.29
5.97 487.03
15.84 464.93
14.84 466.0
12.25 469.52
27.38 428.88
8.76 474.3
15.54 461.06
18.71 465.57
13.06 467.67
12.72 466.99
19.83 463.72
27.23 443.78
24.27 445.23
11.8 464.43
6.76 484.36
25.99 442.16
16.3 464.11
16.5 462.48
10.59 477.49
26.05 437.04
19.5 457.09
22.21 450.6
17.86 465.78
29.96 427.1
19.08 459.81
23.59 447.36
3.38 488.92
26.39 433.36
8.99 483.35
10.91 469.53
13.08 476.96
23.95 440.75
15.64 462.55
18.78 448.04
20.65 455.24
4.96 494.75
23.51 444.58
5.99 484.82
23.65 442.9
5.17 485.46
26.38 457.81
6.02 481.92
23.2 443.23
8.57 474.29
30.72 430.46
21.52 455.71
22.93 438.34
5.71 485.83
18.62 452.82
27.88 435.04
22.32 451.21
14.55 465.81
17.83 458.42
9.68 470.22
19.41 449.24
13.22 471.43
12.24 473.26
19.21 452.82
29.74 432.69
23.28 444.13
8.02 467.21
22.47 445.98
27.51 436.91
17.51 455.01
23.22 437.11
11.73 477.06
21.19 441.71
5.48 495.76
24.26 445.63
12.32 464.72
31.26 438.03
32.09 434.78
24.98 444.67
27.48 452.24
21.04 450.92
27.75 436.53
22.79 435.53
24.22 440.01
27.06 443.1
29.25 427.49
26.86 436.25
29.64 440.74
19.92 443.54
18.5 459.42
23.71 439.66
14.39 464.15
19.3 459.1
24.65 455.68
13.5 469.08
9.82 478.02
18.4 456.8
28.12 441.13
17.15 463.88
30.69 430.45
28.82 449.18
21.3 447.89
30.58 431.59
21.17 447.5
9.87 475.58
22.18 453.24
24.39 446.4
10.73 476.81
9.38 474.1
20.27 450.71
24.82 433.62
16.55 465.14
20.73 445.18
9.51 474.12
8.63 483.91
6.48 486.68
14.95 464.98
5.76 481.4
10.94 479.2
15.87 463.86
12.42 472.3
29.12 446.51
29.12 437.71
19.08 458.94
31.06 437.91
5.72 490.76
26.52 439.66
13.84 463.27
13.03 473.99
25.94 433.38
16.64 459.01
14.13 471.44
13.65 471.91
14.5 465.15
19.8 446.66
25.2 438.15
20.66 447.14
12.07 472.32
25.64 441.68
23.33 440.04
29.41 444.82
16.6 457.26
27.53 428.83
20.62 449.07
26.02 435.21
12.75 471.03
12.87 465.56
25.77 442.83
14.84 460.3
7.41 474.25
8.87 477.97
9.69 472.16
16.17 456.08
26.24 452.41
13.78 463.71
26.3 433.72
17.37 456.4
23.6 448.43
8.3 481.6
18.86 457.07
22.12 451.0
28.41 440.28
29.42 437.47
18.61 443.57
27.57 426.6
12.83 470.87
9.64 478.37
19.13 453.92
15.92 470.22
24.64 434.54
27.62 442.89
8.9 479.03
9.55 476.06
10.57 473.88
19.8 451.75
25.63 439.2
24.7 439.7
15.26 463.6
20.06 447.47
19.84 447.92
11.49 471.08
23.74 437.55
22.62 448.27
29.53 431.69
21.32 449.09
20.3 448.79
16.97 460.21
12.07 479.28
7.46 483.11
19.2 450.75
28.64 437.97
13.56 459.76
17.4 457.75
14.08 469.33
27.11 433.28
20.92 444.64
16.18 463.1
15.57 460.91
10.37 479.35
19.6 449.23
9.22 474.51
27.76 435.02
28.68 435.45
20.95 452.38
9.06 480.41
9.21 478.96
13.65 468.87
31.79 434.01
14.32 466.36
26.28 435.28
7.69 486.46
14.44 468.19
9.19 468.37
13.35 474.19
23.04 440.32
4.83 485.32
17.29 464.27
8.73 479.25
26.21 430.4
23.72 447.49
29.27 438.23
10.4 492.09
12.19 475.36
20.4 452.56
34.3 427.84
27.56 433.95
30.9 435.27
14.85 454.62
16.42 472.17
16.45 452.42
10.14 472.17
9.53 481.83
17.01 458.78
23.94 447.5
15.95 463.4
11.15 473.57
25.56 433.72
27.16 431.85
26.71 433.47
29.56 432.84
31.19 436.6
6.86 490.23
12.36 477.16
32.82 441.06
25.3 440.86
8.71 477.94
13.34 474.47
14.2 470.67
23.74 447.31
16.9 466.8
28.54 430.91
30.15 434.75
14.33 469.52
25.57 438.9
30.55 429.56
28.04 432.92
26.39 442.87
15.3 466.59
6.03 479.61
13.49 471.08
27.67 433.37
24.19 443.92
24.44 443.5
29.86 439.89
30.2 434.66
7.99 487.57
9.93 464.64
11.03 470.92
22.34 444.39
25.33 442.48
18.87 449.61
25.97 435.02
16.58 458.67
14.35 461.74
25.06 438.31
13.85 462.38
16.09 460.56
26.34 439.22
23.01 444.64
26.39 430.34
31.32 430.46
16.64 456.79
13.42 468.82
20.06 448.51
14.8 470.77
12.59 465.74
26.7 430.21
19.78 449.23
15.17 461.89
21.71 445.72
19.09 466.13
19.76 448.71
14.68 469.25
21.3 450.56
16.73 464.46
12.26 471.13
14.77 461.52
18.26 451.09
27.1 431.51
14.72 469.8
26.3 442.28
16.48 458.67
17.99 462.4
20.34 453.54
25.53 444.38
31.59 440.52
30.8 433.62
10.75 481.96
19.3 452.75
4.71 481.28
23.1 439.03
32.63 435.75
26.63 436.03
24.35 445.6
15.11 462.65
29.1 438.66
21.24 447.32
6.16 484.55
7.36 476.8
10.44 480.34
26.76 440.63
16.79 459.48
10.76 490.78
6.07 483.56
27.33 429.38
27.15 440.27
22.35 445.34
21.82 447.43
21.11 439.91
19.95 459.27
7.45 478.89
15.36 466.7
15.65 463.5
25.31 436.21
25.88 443.94
24.6 439.63
22.58 460.95
19.69 448.69
25.85 444.63
10.06 473.51
18.59 462.56
18.27 451.76
8.85 491.81
30.04 429.52
26.06 437.9
14.8 467.54
23.93 449.97
23.72 436.62
11.44 477.68
20.28 447.26
27.9 439.76
24.74 437.49
14.8 455.14
8.22 485.5
27.56 444.1
32.07 432.33
9.53 471.23
13.61 463.89
22.2 445.54
21.36 446.09
23.25 445.12
23.5 443.31
8.46 484.16
8.19 477.76
30.67 430.28
32.48 446.48
8.99 481.03
13.77 466.07
19.05 447.47
21.19 455.93
10.12 479.62
24.93 455.06
8.47 475.06
24.52 438.89
28.55 432.7
20.58 452.6
18.31 451.75
27.18 430.66
4.43 491.9
26.02 439.82
15.75 460.73
22.99 449.7
25.52 439.42
27.04 439.84
6.42 485.86
17.04 458.1
10.79 479.92
20.41 458.29
7.36 489.45
28.08 434.0
24.74 431.24
28.32 439.5
16.71 467.46
30.7 429.27
18.42 452.1
10.62 472.41
22.18 442.14
22.38 441.0
13.94 463.07
21.24 445.71
6.76 483.16
26.73 440.45
7.24 481.83
10.84 467.6
19.32 450.88
29.0 425.5
23.38 451.87
31.17 428.94
26.17 439.86
30.9 433.44
24.92 438.23
32.77 436.95
14.37 470.19
8.36 484.66
31.45 430.81
31.6 433.37
17.9 453.02
20.35 453.5
16.21 463.09
19.36 464.56
21.04 452.12
14.05 470.9
23.48 450.89
21.91 445.04
24.42 444.72
14.26 460.38
21.38 446.8
15.71 465.05
5.78 484.13
6.77 488.27
23.84 447.09
21.17 452.02
19.94 455.55
8.73 480.99
16.39 467.68
ExhaustVaccum Power
41.76 463.26
62.96 444.37
39.4 488.56
57.32 446.48
37.5 473.9
59.44 443.67
43.96 467.35
44.71 478.42
45.0 475.98
43.56 477.5
43.72 453.02
46.93 453.99
73.5 440.29
58.59 451.28
69.34 433.99
43.79 462.19
45.0 467.54
41.74 477.2
52.75 459.85
38.47 464.3
42.42 468.27
40.07 495.24
42.28 483.8
63.9 443.61
48.6 436.06
70.72 443.25
39.31 464.16
39.96 475.52
35.79 484.41
65.18 437.89
63.94 445.11
58.41 438.86
66.85 440.98
74.16 436.65
63.94 444.26
44.03 465.86
63.73 444.37
47.45 450.69
39.35 469.02
51.3 448.86
47.45 447.14
44.85 469.18
41.54 482.8
42.86 476.7
40.64 474.99
63.94 444.22
37.87 461.33
43.43 448.06
44.71 474.6
40.11 473.05
73.5 432.06
38.62 467.41
78.92 430.12
42.18 473.62
39.39 471.81
59.43 442.99
64.44 442.77
39.33 491.49
61.08 447.46
58.84 446.11
52.3 442.44
65.71 446.22
40.1 471.49
45.87 463.5
58.79 440.01
65.34 441.03
62.96 452.68
40.69 474.91
34.03 478.77
74.34 434.2
68.3 437.91
41.01 477.61
74.67 431.65
74.34 430.57
42.28 481.09
61.02 445.56
39.85 475.74
69.75 435.12
67.25 446.15
76.86 436.64
69.45 436.69
42.18 468.75
43.02 466.6
45.08 465.48
73.68 441.34
69.45 441.83
39.3 464.7
69.75 437.99
58.96 459.12
68.94 429.69
51.43 459.8
64.05 433.63
60.95 442.84
41.66 485.13
52.72 459.12
67.32 445.31
40.72 480.8
66.48 432.55
63.77 443.86
59.21 449.77
43.69 470.71
51.3 452.17
41.38 478.29
71.94 428.54
40.64 478.27
62.66 439.58
49.69 457.32
44.2 475.51
65.59 439.66
40.89 471.99
39.35 479.81
78.92 434.78
61.87 446.58
58.33 437.76
39.72 459.36
44.71 462.28
43.48 464.33
46.21 444.36
59.39 438.64
34.03 470.49
41.1 455.13
66.93 450.22
63.73 440.43
42.85 482.98
50.88 460.44
54.2 444.97
68.67 433.94
73.18 439.73
73.88 434.48
60.84 442.33
45.09 457.67
57.76 454.66
67.25 432.21
43.77 457.66
63.76 435.21
47.43 448.22
41.66 475.51
62.91 446.53
57.32 441.3
69.34 433.54
39.04 472.52
44.78 474.77
69.05 435.1
59.8 450.74
65.06 442.7
68.14 426.56
42.32 463.71
66.93 447.06
57.76 452.27
63.94 445.78
68.3 438.65
40.77 480.15
62.52 447.19
48.41 443.04
39.9 488.81
57.76 455.75
49.39 455.86
46.97 457.68
40.05 479.11
74.16 432.84
62.52 448.37
47.45 447.06
49.21 443.53
61.45 445.21
66.51 441.7
66.86 450.93
49.78 451.44
56.89 441.29
44.85 458.85
43.65 481.46
43.41 467.19
41.58 461.54
52.84 439.08
42.74 467.22
44.34 468.8
71.98 426.93
37.73 474.65
44.21 468.97
71.58 433.97
50.16 450.53
59.04 444.51
40.69 469.03
71.14 466.56
52.05 457.57
59.54 440.13
69.84 433.24
43.72 452.55
71.37 443.29
74.99 431.76
44.78 454.97
63.07 456.7
39.9 486.03
39.96 472.79
57.25 452.03
54.2 443.41
67.32 441.93
70.98 432.64
36.24 480.25
39.63 466.68
40.07 494.39
54.42 454.72
56.85 448.71
42.48 469.76
44.89 450.71
58.79 444.01
58.2 453.2
57.85 450.87
63.86 441.73
39.52 465.09
64.63 447.28
39.33 491.16
62.52 450.98
63.56 446.3
79.74 436.48
42.03 460.84
69.51 442.56
41.49 467.3
40.64 479.13
52.3 441.15
59.04 445.52
40.69 475.4
40.69 469.3
41.62 463.57
68.67 445.32
44.21 461.03
43.13 466.74
59.87 444.04
67.25 434.01
44.9 465.23
69.34 440.6
44.84 466.74
75.6 433.48
40.96 473.59
41.74 474.81
44.06 454.75
49.69 452.94
69.13 435.83
39.22 482.19
44.47 466.66
40.12 462.59
64.63 447.82
41.62 462.73
63.21 447.98
39.31 462.72
58.46 442.42
48.41 444.69
39.0 466.7
64.63 453.84
67.32 436.92
36.08 486.37
60.07 440.43
63.07 446.82
39.48 484.91
71.64 437.76
68.08 438.91
39.54 464.19
67.79 442.19
47.43 446.86
44.2 457.15
43.65 482.57
41.26 476.03
72.58 428.89
40.23 472.7
52.3 445.6
52.05 464.78
58.95 440.42
70.79 428.41
70.47 438.5
59.43 438.28
40.64 476.29
58.49 448.46
57.19 438.99
39.22 471.8
42.44 471.81
59.14 449.82
68.08 442.14
58.79 441.46
40.22 477.62
61.87 446.76
39.82 472.52
40.71 471.58
72.39 440.85
77.54 431.37
71.97 437.33
40.71 469.22
40.78 471.11
66.51 439.17
62.26 445.33
39.22 473.71
57.76 452.66
59.43 440.99
38.91 467.42
58.82 444.14
56.53 457.17
54.3 467.87
70.72 442.04
44.58 471.36
39.1 460.7
70.83 431.33
76.86 432.6
59.39 447.61
64.79 443.87
52.3 446.87
41.01 465.74
56.89 447.86
62.96 447.65
63.47 437.87
41.66 483.51
41.46 479.65
58.16 455.16
71.25 431.91
41.5 470.68
69.13 429.28
59.21 450.81
73.5 437.73
44.6 460.21
69.05 442.86
45.0 482.99
65.27 440.0
41.82 478.48
49.82 455.28
66.56 436.94
37.87 461.06
69.45 438.28
39.58 472.61
70.79 426.85
39.72 470.18
54.42 455.38
66.56 428.32
42.49 480.35
56.53 455.56
58.33 447.66
68.67 443.06
58.86 452.43
41.54 477.81
73.77 431.66
73.91 431.8
68.67 446.67
60.29 445.26
69.89 425.72
72.29 430.58
60.27 439.86
59.15 441.11
71.14 434.72
67.45 434.01
41.17 475.64
41.58 460.44
68.67 436.4
40.73 461.03
41.54 479.08
69.14 435.76
45.51 460.14
75.6 442.2
50.78 447.69
73.67 431.15
63.56 445.0
73.21 431.59
44.47 467.22
51.43 445.33
45.09 470.57
39.61 473.77
60.29 447.67
40.05 474.29
71.32 437.14
69.05 432.56
41.79 459.14
60.07 446.19
71.77 428.1
44.47 468.46
66.75 435.02
48.6 445.52
40.55 462.69
61.27 455.75
40.0 463.74
69.68 439.79
58.95 443.26
69.13 432.04
43.96 465.86
45.87 465.6
25.36 469.43
49.3 440.75
38.91 481.32
40.56 479.87
39.16 458.59
71.97 438.62
59.44 445.59
43.56 481.87
41.5 475.01
48.41 436.54
40.66 456.63
62.52 451.69
39.59 463.04
67.71 446.1
59.92 438.67
41.4 466.88
48.41 444.6
59.39 440.26
40.96 483.92
41.2 475.19
44.68 479.24
73.68 434.92
65.46 454.16
58.79 447.58
41.26 467.9
69.13 426.29
45.87 447.02
41.79 455.85
40.22 476.46
68.94 437.48
49.21 452.77
39.33 491.54
64.79 438.41
41.93 476.1
36.99 464.58
40.78 467.74
57.17 442.12
57.5 453.34
69.13 425.29
51.43 449.63
45.78 462.88
42.32 464.67
35.57 489.96
38.08 482.38
77.95 437.95
71.98 429.2
46.63 453.34
70.02 442.47
49.69 462.6
40.96 478.79
52.05 456.11
50.16 450.33
69.88 434.83
73.68 433.43
62.96 456.02
40.67 485.23
37.14 473.57
39.58 469.94
49.83 452.07
41.04 475.32
36.24 480.69
48.06 444.01
56.03 465.17
39.82 480.61
41.5 476.04
49.3 441.76
71.98 428.24
67.71 444.77
45.87 463.1
44.6 470.5
72.99 431.0
69.4 430.68
67.17 436.42
49.82 452.33
63.94 440.16
63.76 435.75
44.57 449.74
72.24 430.73
77.17 432.75
47.43 446.79
41.39 486.35
59.8 453.18
50.23 458.31
41.54 480.26
51.3 448.65
49.5 458.41
64.69 435.39
50.16 450.21
43.14 459.59
75.6 445.84
48.78 441.08
37.85 467.33
63.09 444.19
68.27 432.96
47.93 438.09
36.99 467.9
43.67 475.72
34.69 477.51
69.84 435.13
39.61 477.9
44.2 457.26
44.6 467.53
41.58 465.15
40.1 474.28
65.71 444.49
45.09 452.84
77.54 435.38
75.33 433.57
69.71 435.27
39.63 468.49
70.8 433.07
73.56 430.63
65.74 440.74
39.96 474.49
48.6 449.74
63.76 436.73
70.79 434.58
43.13 473.93
79.74 435.99
43.22 466.83
68.24 427.22
63.77 444.07
41.49 469.57
66.51 459.89
40.71 479.59
64.69 440.92
41.46 480.87
66.54 441.9
69.68 430.2
42.86 465.16
44.45 471.32
40.64 485.43
40.07 495.35
58.49 449.12
42.02 480.53
50.66 457.07
69.94 443.67
44.68 477.52
39.64 472.95
39.69 472.54
40.71 469.17
70.04 435.21
40.22 477.78
39.85 475.89
40.23 483.9
39.16 476.2
43.34 462.16
37.85 471.05
41.26 484.71
63.86 446.34
42.28 469.02
72.86 432.12
40.1 467.28
69.98 429.66
45.09 469.49
40.56 485.87
40.81 481.95
40.02 479.03
69.13 434.5
54.3 464.9
63.94 452.71
69.51 429.74
56.65 457.09
72.29 446.77
41.48 460.76
40.83 471.95
46.21 453.29
60.07 441.61
46.18 464.73
43.69 464.68
73.91 430.59
77.95 438.01
41.04 479.08
74.78 436.39
65.46 447.07
39.58 479.91
43.02 489.05
53.82 463.17
42.02 471.26
40.67 480.49
39.42 473.78
58.16 455.5
58.41 446.27
41.06 482.2
59.8 452.48
43.14 464.48
69.89 438.1
63.47 445.6
67.79 442.43
61.25 436.67
41.85 466.56
71.14 457.29
36.25 487.03
52.72 464.93
44.63 466.0
48.79 469.52
70.04 428.88
41.48 474.3
39.31 461.06
39.39 465.57
41.78 467.67
40.71 466.99
39.39 463.72
49.16 443.78
68.28 445.23
40.66 464.43
36.25 484.36
63.07 442.16
39.63 464.11
49.39 462.48
42.49 477.49
65.59 437.04
40.79 457.09
45.01 450.6
45.0 465.78
70.04 427.1
44.63 459.81
47.43 447.36
39.64 488.92
66.49 433.36
39.04 483.35
41.04 469.53
39.82 476.96
58.46 440.75
43.71 462.55
54.2 448.04
50.59 455.24
40.07 494.75
57.32 444.58
35.79 484.82
66.05 442.9
39.33 485.46
49.5 457.81
43.65 481.92
61.02 443.23
39.69 474.29
71.58 430.46
50.66 455.71
62.26 438.34
41.31 485.83
44.06 452.82
68.94 435.04
59.8 451.21
42.74 465.81
44.92 458.42
39.96 470.22
49.39 449.24
44.92 471.43
44.92 473.26
58.49 452.82
70.32 432.69
60.84 444.13
41.92 467.21
48.6 445.98
73.77 436.91
44.9 455.01
66.56 437.11
40.64 477.06
67.71 441.71
40.07 495.76
66.44 445.63
41.62 464.72
68.94 438.03
72.86 434.78
60.32 444.67
61.41 452.24
45.09 450.92
70.4 436.53
71.77 435.53
68.51 440.01
64.45 443.1
71.94 427.49
68.08 436.25
67.79 440.74
63.31 443.54
51.43 459.42
60.23 439.66
44.84 464.15
56.65 459.1
52.36 455.68
45.51 469.08
41.26 478.02
44.06 456.8
44.89 441.13
43.69 463.88
73.67 430.45
65.71 449.18
48.92 447.89
70.04 431.59
52.3 447.5
41.82 475.58
59.8 453.24
63.21 446.4
44.92 476.81
40.46 474.1
57.76 450.71
66.48 433.62
41.66 465.14
59.87 445.18
39.22 474.12
43.79 483.91
40.27 486.68
43.52 464.98
45.87 481.4
39.04 479.2
41.16 463.86
38.25 472.3
58.84 446.51
51.43 437.71
41.1 458.94
67.17 437.91
39.33 490.76
65.06 439.66
44.9 463.27
39.52 473.99
66.49 433.38
53.82 459.01
40.75 471.44
39.28 471.91
44.47 465.15
51.19 446.66
63.76 438.15
51.19 447.14
43.71 472.32
70.72 441.68
72.99 440.04
64.05 444.82
53.16 457.26
72.58 428.83
43.43 449.07
71.94 435.21
44.2 471.03
48.04 465.56
62.96 442.83
41.48 460.3
40.71 474.25
41.82 477.97
40.46 472.16
46.97 456.08
49.82 452.41
43.22 463.71
67.07 433.72
57.76 456.4
48.98 448.43
36.08 481.6
42.18 457.07
49.39 451.0
75.6 440.28
71.32 437.47
67.71 443.57
69.84 426.6
41.5 470.87
39.85 478.37
58.66 453.92
40.56 470.22
72.24 434.54
63.9 442.89
36.24 479.03
43.99 476.06
36.71 473.88
57.25 451.75
56.85 439.2
58.46 439.7
46.18 463.6
52.84 447.47
56.89 447.92
44.63 471.08
72.43 437.55
51.3 448.27
72.39 431.69
48.14 449.09
58.46 448.79
44.92 460.21
41.17 479.28
41.82 483.11
54.2 450.75
66.54 437.97
41.48 459.76
44.9 457.75
40.1 469.33
69.75 433.28
70.02 444.64
44.9 463.1
44.68 460.91
39.04 479.35
59.21 449.23
40.92 474.51
72.99 435.02
70.72 435.45
48.14 452.38
39.3 480.41
39.72 478.96
42.74 468.87
76.2 434.01
44.6 466.36
75.23 435.28
43.02 486.46
40.1 468.19
41.01 468.37
41.39 474.19
74.22 440.32
38.44 485.32
42.86 464.27
36.18 479.25
70.32 430.4
58.62 447.49
64.69 438.23
40.43 492.09
40.75 475.36
54.9 452.56
74.67 427.84
68.08 433.95
70.8 435.27
58.59 454.62
40.56 472.17
63.31 452.42
42.02 472.17
41.44 481.83
49.15 458.78
62.08 447.5
49.25 463.4
41.26 473.57
70.32 433.72
66.44 431.85
77.95 433.47
74.22 432.84
70.94 436.6
41.17 490.23
41.74 477.16
68.31 441.06
70.98 440.86
41.82 477.94
40.8 474.47
43.02 470.67
65.34 447.31
44.88 466.8
71.94 430.91
69.88 434.75
42.86 469.52
59.43 438.9
70.04 429.56
74.33 432.92
49.16 442.87
41.76 466.59
41.14 479.61
44.63 471.08
59.14 433.37
65.48 443.92
59.14 443.5
64.79 439.89
69.59 434.66
41.38 487.57
41.62 464.64
42.32 470.92
63.73 444.39
48.6 442.48
52.08 449.61
69.34 435.02
43.99 458.67
46.18 461.74
62.39 438.31
48.92 462.38
44.2 460.56
59.21 439.22
58.79 444.64
71.25 430.34
71.29 430.46
45.87 456.79
41.23 468.82
44.9 448.51
44.71 470.77
41.14 465.74
66.56 430.21
50.32 449.23
49.15 461.89
61.45 445.72
39.39 466.13
51.19 448.71
41.23 469.25
66.86 450.56
39.64 464.46
41.5 471.13
48.06 461.52
59.15 451.09
79.74 431.51
40.83 469.8
51.43 442.28
48.92 458.67
43.79 462.4
59.8 453.54
62.96 444.38
58.9 440.52
69.14 433.62
45.0 481.96
44.9 452.75
39.42 481.28
66.05 439.03
73.88 435.75
74.16 436.03
58.49 445.6
56.03 462.65
50.05 438.66
50.32 447.32
39.48 484.55
41.01 476.8
39.04 480.34
48.41 440.63
44.6 459.48
40.43 490.78
38.91 483.56
73.18 429.38
59.21 440.27
51.43 445.34
65.27 447.43
69.94 439.91
50.59 459.27
39.61 478.89
41.66 466.7
43.5 463.5
74.33 436.21
63.47 443.94
63.94 439.63
41.54 460.95
59.14 448.69
75.08 444.63
37.83 473.51
39.54 462.56
50.16 451.76
40.43 491.81
68.08 429.52
49.02 437.9
38.73 467.54
64.45 449.97
66.48 436.62
40.55 477.68
63.86 447.26
63.13 439.76
59.39 437.49
58.2 455.14
41.03 485.5
66.93 444.1
70.94 432.33
44.03 471.23
42.34 463.89
51.19 445.54
59.54 446.09
63.86 445.12
59.21 443.31
39.66 484.16
40.69 477.76
71.29 430.28
62.04 446.48
36.66 481.03
47.83 466.07
67.32 447.47
55.5 455.93
40.0 479.62
47.01 455.06
40.46 475.06
56.85 438.89
69.84 432.7
50.9 452.6
46.21 451.75
71.06 430.66
38.91 491.9
74.78 439.82
39.0 460.73
60.95 449.7
59.15 439.42
65.06 439.84
35.57 485.86
40.12 458.1
39.82 479.92
56.03 458.29
40.07 489.45
73.42 434.0
69.13 431.24
47.93 439.5
40.56 467.46
71.58 429.27
58.95 452.1
42.02 472.41
69.05 442.14
49.3 441.0
41.58 463.07
60.84 445.71
39.81 483.16
68.84 440.45
38.06 481.83
40.62 467.6
52.84 450.88
69.13 425.5
54.42 451.87
69.51 428.94
48.6 439.86
73.42 433.44
73.68 438.23
71.32 436.95
40.56 470.19
40.22 484.66
68.27 430.81
73.17 433.37
48.98 453.02
50.9 453.5
41.23 463.09
44.6 464.56
65.46 452.12
40.69 470.9
64.15 450.89
63.76 445.04
63.07 444.72
40.92 460.38
58.33 446.8
44.06 465.05
40.62 484.13
39.81 488.27
49.21 447.09
58.16 452.02
58.96 455.55
41.92 480.99
41.67 467.68
Pressure Power
1024.07 463.26
1020.04 444.37
1012.16 488.56
1010.24 446.48
1009.23 473.9
1012.23 443.67
1014.02 467.35
1019.12 478.42
1021.78 475.98
1015.14 477.5
1008.64 453.02
1014.66 453.99
1011.31 440.29
1012.77 451.28
1009.48 433.99
1015.76 462.19
1022.86 467.54
1022.6 477.2
1023.97 459.85
1015.15 464.3
1009.09 468.27
1019.16 495.24
1008.52 483.8
1014.3 443.61
1003.18 436.06
1009.97 443.25
1011.11 464.16
1023.57 475.52
1012.14 484.41
1012.69 437.89
1019.02 445.11
1013.64 438.86
1011.11 440.98
1010.08 436.65
1018.76 444.26
1007.29 465.86
1011.4 444.37
1010.08 450.69
1014.69 469.02
1012.04 448.86
1007.62 447.14
1017.24 469.18
1018.33 482.8
1014.12 476.7
1020.63 474.99
1019.28 444.22
1020.24 461.33
1010.96 448.06
1014.51 474.6
1029.14 473.05
1010.58 432.06
1018.71 467.41
1011.6 430.12
1014.82 473.62
1012.94 471.81
1010.23 442.99
1014.65 442.77
1010.18 491.49
1013.68 447.46
1002.25 446.11
1007.03 442.44
1013.61 446.22
1016.67 471.49
1008.89 463.5
1016.02 440.01
1014.56 441.03
1019.49 452.68
1020.43 474.91
1018.71 478.77
998.14 434.2
1017.83 437.91
1024.61 477.61
1016.65 431.65
998.58 430.57
1008.82 481.09
1009.56 445.56
1012.71 475.74
1010.36 435.12
1017.39 446.15
1001.31 436.64
1013.89 436.69
1016.53 468.75
1012.18 466.6
1024.42 465.48
1014.93 441.34
1012.53 441.83
1019.0 464.7
1009.6 437.99
1015.55 459.12
1006.56 429.69
1010.57 459.8
1009.81 433.63
1014.62 442.84
1014.49 485.13
1025.09 459.12
1012.05 445.31
1022.7 480.8
1005.23 432.55
1013.42 443.86
1018.3 449.77
1017.19 470.71
1011.93 452.17
1021.6 478.29
1006.96 428.54
1020.66 478.27
1007.63 439.58
1005.53 457.32
1018.79 475.51
1010.85 439.66
1011.03 471.99
1015.1 479.81
1010.83 434.78
1011.18 446.58
1013.92 437.76
1001.24 459.36
1016.99 462.28
1016.08 464.33
1010.71 444.36
1014.32 438.64
1018.69 470.49
1001.86 455.13
1017.06 450.22
1009.34 440.43
1014.02 482.98
1014.19 460.44
1012.81 444.97
1005.2 433.94
1012.28 439.73
1005.89 434.48
1017.93 442.33
1014.26 457.67
1018.8 454.66
1017.71 432.21
1012.25 457.66
1010.27 435.21
1007.64 448.22
1013.79 475.51
1013.24 446.53
1011.7 441.3
1007.67 433.54
1020.45 472.52
1012.59 474.77
1001.62 435.1
1016.92 450.74
1014.32 442.7
1003.34 426.56
1016.0 463.71
1016.85 447.06
1018.02 452.27
1018.49 445.78
1017.93 438.65
1011.55 480.15
1016.46 447.19
1008.47 443.04
1008.06 488.81
1016.26 455.75
1018.83 455.86
1013.94 457.68
1014.95 479.11
1007.44 432.84
1016.18 448.37
1007.56 447.06
1014.1 443.53
1011.13 445.21
1015.53 441.7
1013.05 450.93
1002.95 451.44
1012.32 441.29
1014.48 458.85
1018.24 481.46
1016.93 467.19
1020.5 461.54
1006.28 439.08
1028.79 467.22
1019.49 468.8
1004.66 426.93
1024.36 474.65
1022.93 468.97
1010.18 433.97
1009.52 450.53
1011.98 444.51
1015.29 469.03
1019.36 466.56
1012.15 457.57
1006.24 440.13
1003.57 433.24
1008.64 452.55
1002.04 443.29
1005.47 431.76
1007.81 454.97
1012.42 456.7
1007.75 486.03
1011.37 472.79
1010.12 452.03
1012.26 443.41
1014.49 441.93
1007.51 432.64
1013.15 480.25
1004.47 466.68
1020.19 494.39
1012.46 454.72
1012.28 448.71
1013.43 469.76
1009.64 450.71
1009.8 444.01
1017.46 453.2
1012.39 450.87
1019.67 441.73
1018.41 465.09
1020.59 447.28
1011.54 491.16
1017.99 450.98
1013.75 446.3
1008.37 436.48
1017.41 460.84
1013.43 442.56
1020.19 467.3
1021.47 479.13
1008.72 441.15
1011.78 445.52
1015.62 475.4
1014.91 469.3
1017.17 463.57
1006.71 445.32
1020.36 461.03
1014.99 466.74
1018.47 444.04
1017.6 434.01
1020.5 465.23
1009.63 440.6
1023.66 466.74
1017.43 433.48
1023.36 473.59
1020.75 474.81
1017.58 454.75
1009.6 452.94
1009.94 435.83
1014.53 482.19
1030.46 466.66
1013.03 462.59
1020.69 447.82
1014.55 462.73
1012.06 447.98
1009.15 462.72
1016.82 442.42
1008.64 444.69
1016.73 466.7
1020.38 453.84
1013.83 436.92
1021.82 486.37
1015.42 440.43
1010.94 446.82
1005.11 484.91
1008.27 437.76
1013.27 438.91
1007.97 464.19
1009.89 442.19
1008.38 446.86
1018.89 457.15
1020.14 482.57
1007.44 476.03
1008.69 428.89
1018.07 472.7
1009.04 445.6
1014.63 464.78
1017.02 440.42
1003.96 428.41
1010.65 438.5
1007.84 438.28
1022.35 476.29
1011.5 448.46
1008.62 438.99
1017.9 471.8
1012.74 471.81
1016.12 449.82
1013.13 442.14
1009.74 441.46
1011.37 477.62
1011.45 446.76
1012.46 472.52
1021.27 471.58
1001.15 440.85
1011.33 431.37
1008.64 437.33
1015.68 469.22
1023.51 471.11
1010.98 439.17
1012.11 445.33
1018.62 473.71
1016.28 452.66
1007.12 440.99
1014.48 467.42
1010.02 444.14
1020.57 457.17
1015.92 467.87
1009.78 442.04
1019.52 471.36
1009.81 460.7
1010.35 431.33
998.59 432.6
1014.07 447.61
1016.27 443.87
1009.2 446.87
1020.57 465.74
1014.02 447.86
1020.16 447.65
1011.78 437.87
1016.87 483.51
1018.21 479.65
1016.88 455.16
1002.49 431.91
1014.13 470.68
1009.29 429.28
1018.32 450.81
1011.49 437.73
1015.16 460.21
1001.6 442.86
1021.51 482.99
1013.27 440.0
1033.25 478.48
1015.01 455.28
1002.07 436.94
1022.36 461.06
1013.97 438.28
1011.81 472.61
1003.7 426.85
1017.76 470.18
1012.31 455.38
1006.44 428.32
1010.57 480.35
1020.2 455.56
1013.14 447.66
1006.74 443.06
1014.19 452.43
1019.94 477.81
1004.72 431.66
1004.53 431.8
1006.65 446.67
1018.0 445.26
1013.85 425.72
1008.73 430.58
1018.94 439.86
1013.31 441.11
1011.6 434.72
1014.23 434.01
1019.43 475.64
1020.43 460.44
1005.46 436.4
1018.7 461.03
1019.94 479.08
1009.31 435.76
1015.22 460.14
1017.37 442.2
1008.83 447.69
1006.65 431.15
1014.32 445.0
1001.32 431.59
1027.94 467.22
1012.16 445.33
1013.21 470.57
1018.72 473.77
1017.42 447.67
1014.78 474.29
1008.07 437.14
1003.12 432.56
1009.72 459.14
1016.03 446.19
1006.38 428.1
1030.18 468.46
1017.95 435.02
1002.38 445.52
1003.36 462.69
1019.26 455.75
1015.89 463.74
1011.95 439.79
1016.99 443.26
1009.3 432.04
1013.32 465.86
1009.05 465.6
1009.35 469.43
1003.4 440.75
1015.82 481.32
1022.64 479.87
1005.7 458.59
1009.62 438.62
1012.38 445.59
1015.13 481.87
1013.58 475.01
1008.53 436.54
1016.28 456.63
1015.63 451.69
1010.93 463.04
1007.68 446.1
1009.94 438.67
1019.7 466.88
1008.42 444.6
1015.4 440.26
1023.28 483.92
1017.18 475.19
1023.06 479.24
1014.95 434.92
1014.0 454.16
1012.42 447.58
1021.67 467.9
1010.27 426.29
1007.8 447.02
1005.47 455.85
1010.31 476.46
1007.53 437.48
1014.61 452.77
1012.57 491.54
1017.22 438.41
1019.81 476.1
1007.87 464.58
1023.91 467.74
1010.0 442.12
1014.53 453.34
1010.44 425.29
1011.74 449.63
1025.27 462.88
1015.71 464.67
1027.17 489.96
1020.27 482.38
1009.45 437.95
1004.74 429.2
1013.03 453.34
1010.58 442.47
1015.14 462.6
1024.57 478.79
1012.34 456.11
1005.81 450.33
1007.21 434.83
1015.1 433.43
1020.76 456.02
1018.08 485.23
1013.03 473.57
1011.17 469.94
1008.69 452.07
1021.82 475.32
1013.39 480.69
1013.12 444.01
1020.41 465.17
1012.87 480.61
1013.39 476.04
1003.51 441.76
1005.19 428.24
1004.0 444.77
1008.58 463.1
1018.19 470.5
1007.04 431.0
1004.27 430.68
1006.6 436.42
1016.19 452.33
1010.64 440.16
1010.18 435.75
1008.48 449.74
1010.74 430.73
1009.55 432.75
1007.88 446.79
1018.12 486.35
1015.66 453.18
1015.73 458.31
1019.7 480.26
1011.89 448.65
1012.67 458.41
1006.37 435.39
1010.49 450.21
1018.68 459.59
1017.19 445.84
1018.17 441.08
1009.89 467.33
1016.56 444.19
1007.96 432.96
1002.85 438.09
1006.86 467.9
1012.68 475.72
1027.72 477.51
1006.37 435.13
1017.99 477.9
1019.18 457.26
1015.88 467.53
1021.08 465.15
1014.42 474.28
1013.85 444.49
1014.15 452.84
1008.5 435.38
1003.88 433.57
1009.04 435.27
1005.35 468.49
1009.9 433.07
1004.85 430.63
1013.29 440.74
1026.31 474.49
1005.72 449.74
1010.09 436.73
1006.53 434.58
1017.24 473.93
1007.03 435.99
1009.45 466.83
1005.29 427.22
1013.39 444.07
1020.11 469.57
1015.18 459.89
1024.91 479.59
1007.21 440.92
1020.45 480.87
1009.93 441.9
1011.35 430.2
1014.62 465.16
1021.19 471.32
1020.91 485.43
1012.27 495.35
1010.85 449.12
1006.22 480.53
1014.89 457.07
1010.7 443.67
1023.44 477.52
1012.52 472.95
1003.92 472.54
1015.85 469.17
1011.09 435.21
1004.81 477.78
1012.86 475.89
1017.75 483.9
1016.53 476.2
1015.47 462.16
1011.24 471.05
1010.6 484.71
1015.43 446.34
1007.21 469.02
1004.23 432.12
1015.51 467.28
1013.29 429.66
1013.16 469.49
1023.23 485.87
1026.0 481.95
1031.1 479.03
1010.99 434.5
1015.16 464.9
1020.02 452.71
1010.84 429.74
1020.67 457.09
1011.61 446.77
1014.46 460.76
1008.31 471.95
1014.09 453.29
1016.34 441.61
1017.01 464.73
1016.91 464.68
1003.72 430.59
1014.19 438.01
1021.84 479.08
1009.28 436.39
1016.25 447.07
1011.9 479.91
1013.88 489.05
1016.46 463.17
1001.18 471.26
1011.64 480.49
1025.41 473.78
1016.76 455.5
1013.78 446.27
1021.21 482.2
1016.72 452.48
1011.92 464.48
1015.29 438.1
1012.77 445.6
1010.37 442.43
1011.56 436.67
1016.54 466.56
1019.65 457.29
1029.65 487.03
1026.45 464.93
1019.28 466.0
1017.44 469.52
1011.18 428.88
1018.49 474.3
1009.69 461.06
1014.09 465.57
1012.3 467.67
1016.02 466.99
1013.73 463.72
1004.03 443.78
1005.43 445.23
1017.13 464.43
1028.31 484.36
1012.5 442.16
1004.64 464.11
1018.35 462.48
1009.59 477.49
1012.78 437.04
1003.8 457.09
1012.22 450.6
1023.25 465.78
1010.15 427.1
1020.14 459.81
1006.64 447.36
1011.0 488.92
1012.96 433.36
1021.99 483.35
1026.57 469.53
1012.27 476.96
1017.5 440.75
1024.51 462.55
1012.05 448.04
1016.22 455.24
1011.8 494.75
1012.55 444.58
1011.56 484.82
1019.6 442.9
1009.68 485.46
1012.82 457.81
1013.85 481.92
1009.63 443.23
1000.91 474.29
1009.98 430.46
1013.56 455.71
1011.25 438.34
1003.24 485.83
1017.76 452.82
1007.68 435.04
1016.82 451.21
1028.41 465.81
1025.04 458.42
1026.09 470.22
1020.84 449.24
1023.84 471.43
1023.74 473.26
1011.7 452.82
1008.1 432.69
1017.91 444.13
1029.8 467.21
1002.33 445.98
1002.42 436.91
1009.05 455.01
1002.47 437.11
1020.68 477.06
1006.65 441.71
1019.63 495.76
1011.33 445.63
1012.88 464.72
1005.94 438.03
1003.47 434.78
1015.63 444.67
1012.2 452.24
1014.19 450.92
1006.65 436.53
1005.75 435.53
1013.23 440.01
1008.72 443.1
1007.18 427.49
1012.99 436.25
1009.99 440.74
1015.02 443.54
1010.82 459.42
1009.76 439.66
1023.55 464.15
1020.55 459.1
1014.76 455.68
1015.33 469.08
1007.71 478.02
1017.36 456.8
1009.18 441.13
1017.05 463.88
1006.14 430.45
1014.24 449.18
1010.92 447.89
1010.4 431.59
1009.36 447.5
1033.04 475.58
1016.77 453.24
1012.59 446.4
1025.1 476.81
1019.29 474.1
1016.66 450.71
1006.4 433.62
1011.45 465.14
1019.08 445.18
1015.3 474.12
1016.08 483.91
1010.55 486.68
1022.43 464.98
1010.83 481.4
1021.81 479.2
1005.85 463.86
1012.76 472.3
1001.31 446.51
1005.93 437.71
1001.96 458.94
1007.62 437.91
1009.96 490.76
1013.4 439.66
1007.58 463.27
1016.68 473.99
1012.83 433.38
1015.13 459.01
1016.05 471.44
1012.97 471.91
1028.2 465.15
1008.25 446.66
1009.78 438.15
1008.81 447.14
1025.53 472.32
1010.16 441.68
1009.33 440.04
1009.82 444.82
1014.5 457.26
1009.13 428.83
1009.93 449.07
1009.38 435.21
1017.59 471.03
1012.47 465.56
1019.86 442.83
1017.26 460.3
1023.07 474.25
1033.3 477.97
1019.1 472.16
1014.22 456.08
1014.9 452.41
1011.31 463.71
1006.26 433.72
1016.0 456.4
1015.41 448.43
1020.63 481.6
1001.16 457.07
1019.8 451.0
1018.48 440.28
1002.26 437.47
1004.07 443.57
1004.91 426.6
1013.12 470.87
1012.9 478.37
1013.32 453.92
1020.79 470.22
1011.37 434.54
1013.11 442.89
1013.29 479.03
1020.5 476.06
1022.62 473.88
1010.84 451.75
1012.68 439.2
1015.58 439.7
1013.68 463.6
1004.21 447.47
1013.23 447.92
1020.44 471.08
1007.99 437.55
1012.36 448.27
998.47 431.69
1016.57 449.09
1015.93 448.79
1025.21 460.21
1013.54 479.28
1032.67 483.11
1011.46 450.75
1010.43 437.97
1008.53 459.76
1020.5 457.75
1015.48 469.33
1009.74 433.28
1010.23 444.64
1021.3 463.1
1022.01 460.91
1023.95 479.35
1017.65 449.23
1021.83 474.51
1007.81 435.02
1009.43 435.45
1013.3 452.38
1019.73 480.41
1019.54 478.96
1026.58 468.87
1007.89 434.01
1013.85 466.36
1011.44 435.28
1014.51 486.46
1015.51 468.19
1022.14 468.37
1019.17 474.19
1009.52 440.32
1015.35 485.32
1014.38 464.27
1013.66 479.25
1007.0 430.4
1016.65 447.49
1006.85 438.23
1025.46 492.09
1015.13 475.36
1016.68 452.56
1015.98 427.84
1010.8 433.95
1008.48 435.27
1014.04 454.62
1020.36 472.17
1015.96 452.42
1003.19 472.17
1018.01 481.83
1021.83 458.78
1022.47 447.5
1019.04 463.4
1022.67 473.57
1009.07 433.72
1011.2 431.85
1012.13 433.47
1007.45 432.84
1007.29 436.6
1020.12 490.23
1020.58 477.16
1010.44 441.06
1007.22 440.86
1033.08 477.94
1026.56 474.47
1012.18 470.67
1013.7 447.31
1018.14 466.8
1007.4 430.91
1007.2 434.75
1010.82 469.52
1008.88 438.9
1010.51 429.56
1013.53 432.92
1005.68 442.87
1022.57 466.59
1028.04 479.61
1019.12 471.08
1016.51 433.37
1018.8 443.92
1016.74 443.5
1017.37 439.89
1008.9 434.66
1021.95 487.57
1013.76 464.64
1017.26 470.92
1014.37 444.39
1002.54 442.48
1005.25 449.61
1009.43 435.02
1021.81 458.67
1016.63 461.74
1008.09 438.31
1011.68 462.38
1019.39 460.56
1013.37 439.22
1009.71 444.64
999.8 430.34
1008.37 430.46
1009.02 456.79
994.17 468.82
1008.79 448.51
1014.67 470.77
1025.79 465.74
1005.31 430.21
1008.62 449.23
1021.91 461.89
1010.97 445.72
1013.36 466.13
1008.38 448.71
998.43 469.25
1013.04 450.56
1008.94 464.46
1014.87 471.13
1010.92 461.52
1012.04 451.09
1005.43 431.51
1009.65 469.8
1012.05 442.28
1011.84 458.67
1016.13 462.4
1015.18 453.54
1019.81 444.38
1003.39 440.52
1007.68 433.62
1023.68 481.96
1008.89 452.75
1026.4 481.28
1020.28 439.03
1005.64 435.75
1009.72 436.03
1011.03 445.6
1020.27 462.65
1005.87 438.66
1008.54 447.32
1004.85 484.55
1024.9 476.8
1023.99 480.34
1010.53 440.63
1014.27 459.48
1025.98 490.78
1019.25 483.56
1012.26 429.38
1013.49 440.27
1011.34 445.34
1013.86 447.43
1004.37 439.91
1016.11 459.27
1017.88 478.89
1012.41 466.7
1021.39 463.5
1015.04 436.21
1011.95 443.94
1012.87 439.63
1013.21 460.95
1015.99 448.69
1006.24 444.63
1005.49 473.51
1008.56 462.56
1011.07 451.76
1025.68 491.81
1011.04 429.52
1007.59 437.9
1003.18 467.54
1015.35 449.97
1003.61 436.62
1023.37 477.68
1016.04 447.26
1011.8 439.76
1015.23 437.49
1018.29 455.14
1021.76 485.5
1016.81 444.1
1006.91 432.33
1008.87 471.23
1017.93 463.89
1009.2 445.54
1007.99 446.09
1017.82 445.12
1018.29 443.31
1015.14 484.16
1019.86 477.76
1008.36 430.28
1010.39 446.48
1028.11 481.03
1007.41 466.07
1013.2 447.47
1019.83 455.93
1021.15 479.62
1014.28 455.06
1019.87 475.06
1012.59 438.89
1003.38 432.7
1011.89 452.6
1010.46 451.75
1008.16 430.66
1019.04 491.9
1010.04 439.82
1015.91 460.73
1015.14 449.7
1013.88 439.42
1013.33 439.84
1025.58 485.86
1011.81 458.1
1012.89 479.92
1019.94 458.29
1017.29 489.45
1012.17 434.0
1010.69 431.24
1003.26 439.5
1019.48 467.46
1010.0 429.27
1016.95 452.1
999.83 472.41
1002.75 442.14
1003.56 441.0
1020.76 463.07
1017.99 445.71
1017.11 483.16
1010.75 440.45
1020.6 481.83
1015.53 467.6
1004.29 450.88
1001.22 425.5
1013.95 451.87
1010.51 428.94
1002.59 439.86
1011.21 433.44
1015.12 438.23
1007.68 436.95
1021.67 470.19
1011.6 484.66
1007.56 430.81
1010.05 433.37
1014.17 453.02
1012.6 453.5
995.88 463.09
1016.25 464.56
1017.22 452.12
1015.66 470.9
1021.08 450.89
1009.85 445.04
1011.49 444.72
1022.07 460.38
1013.05 446.8
1018.34 465.05
1016.55 484.13
1017.01 488.27
1013.85 447.09
1017.16 452.02
1014.16 455.55
1029.41 480.99
1012.96 467.68
Humidity Power
73.17 463.26
59.08 444.37
92.14 488.56
76.64 446.48
96.62 473.9
58.77 443.67
75.24 467.35
66.43 478.42
41.25 475.98
70.72 477.5
75.04 453.02
64.22 453.99
84.15 440.29
61.83 451.28
87.59 433.99
43.08 462.19
48.84 467.54
77.51 477.2
63.59 459.85
55.28 464.3
66.26 468.27
64.77 495.24
83.31 483.8
47.19 443.61
54.93 436.06
74.62 443.25
72.52 464.16
88.44 475.52
92.28 484.41
41.85 437.89
44.28 445.11
64.58 438.86
63.25 440.98
78.61 436.65
44.51 444.26
89.46 465.86
74.52 444.37
88.86 450.69
75.51 469.02
78.64 448.86
76.65 447.14
80.44 469.18
79.89 482.8
88.28 476.7
84.6 474.99
42.69 444.22
78.41 461.33
61.07 448.06
50.0 474.6
77.29 473.05
43.66 432.06
83.8 467.41
66.47 430.12
93.09 473.62
80.52 471.81
68.99 442.99
57.27 442.77
95.53 491.49
71.72 447.46
57.88 446.11
63.34 442.44
48.07 446.22
91.87 471.49
87.27 463.5
64.4 440.01
43.4 441.03
72.24 452.68
90.22 474.91
74.0 478.77
71.85 434.2
86.62 437.91
97.41 477.61
84.44 431.65
81.55 430.57
75.66 481.09
79.41 445.56
58.91 475.74
90.06 435.12
79.0 446.15
69.47 436.64
51.47 436.69
83.13 468.75
40.33 466.6
81.69 465.48
94.55 441.34
91.81 441.83
63.62 464.7
49.35 437.99
69.61 459.12
38.75 429.69
90.17 459.8
81.24 433.63
48.46 442.84
76.72 485.13
51.16 459.12
76.34 445.31
67.3 480.8
52.38 432.55
76.44 443.86
91.55 449.77
71.9 470.71
80.05 452.17
63.77 478.29
62.26 428.54
89.04 478.27
58.02 439.58
81.82 457.32
91.14 475.51
88.92 439.66
84.83 471.99
91.76 479.81
86.56 434.78
57.21 446.58
54.25 437.76
63.8 459.36
33.71 462.28
67.25 464.33
60.11 444.36
74.55 438.64
67.34 470.49
42.75 455.13
55.2 450.22
83.61 440.43
88.78 482.98
100.12 460.44
64.52 444.97
51.41 433.94
85.78 439.73
75.41 434.48
81.63 442.33
51.92 457.67
70.12 454.66
53.83 432.21
77.23 457.66
65.67 435.21
71.18 448.22
81.96 475.51
79.54 446.53
47.09 441.3
57.69 433.54
78.89 472.52
85.29 474.77
40.13 435.1
77.06 450.74
67.38 442.7
62.44 426.56
77.43 463.71
58.77 447.06
67.72 452.27
42.14 445.78
84.16 438.65
89.79 480.15
67.21 447.19
72.14 443.04
97.49 488.81
87.74 455.75
96.3 455.86
61.25 457.68
88.38 479.11
74.77 432.84
68.18 448.37
77.2 447.06
49.54 443.53
92.22 445.21
33.65 441.7
64.59 450.93
100.09 451.44
68.04 441.29
48.94 458.85
74.47 481.46
81.02 467.19
71.17 461.54
53.85 439.08
70.67 467.22
59.36 468.8
57.17 426.93
70.29 474.65
83.37 468.97
87.36 433.97
100.09 450.53
68.78 444.51
70.98 469.03
75.68 466.56
47.49 457.57
71.99 440.13
66.55 433.24
74.73 452.55
64.78 443.29
75.13 431.76
56.38 454.97
94.35 456.7
86.55 486.03
82.95 472.79
88.42 452.03
85.61 443.41
58.39 441.93
74.28 432.64
87.85 480.25
83.5 466.68
65.24 494.39
75.01 454.72
84.52 448.71
80.52 469.76
75.14 450.71
75.75 444.01
76.72 453.2
85.47 450.87
57.95 441.73
78.32 465.09
52.2 447.28
93.69 491.16
75.74 450.98
67.56 446.3
69.46 436.48
74.58 460.84
53.23 442.56
88.72 467.3
96.16 479.13
68.26 441.15
86.39 445.52
85.34 475.4
72.64 469.3
97.82 463.57
77.22 445.32
80.59 461.03
46.91 466.74
57.76 444.04
53.09 434.01
84.31 465.23
71.58 440.6
92.97 466.74
74.55 433.48
78.96 473.59
64.44 474.81
68.23 454.75
70.81 452.94
61.66 435.83
77.76 482.19
69.49 466.66
96.26 462.59
55.74 447.82
95.61 462.73
84.75 447.98
75.3 462.72
67.5 442.42
80.92 444.69
79.23 466.7
81.1 453.84
32.8 436.92
84.31 486.37
46.15 440.43
53.96 446.82
59.83 484.91
75.3 437.76
42.53 438.91
70.58 464.19
91.69 442.19
63.55 446.86
61.51 457.15
69.55 482.57
98.08 476.03
79.34 428.89
81.28 472.7
78.99 445.6
80.38 464.78
51.16 440.42
72.17 428.41
75.39 438.5
68.91 438.28
96.38 476.29
70.54 448.46
45.8 438.99
57.95 471.8
81.89 471.81
69.32 449.82
59.14 442.14
81.54 441.46
85.81 477.62
65.41 446.76
81.15 472.52
95.87 471.58
90.24 440.85
75.13 431.37
88.22 437.33
81.48 469.22
89.84 471.11
43.57 439.17
63.16 445.33
57.14 473.71
77.76 452.66
90.56 440.99
60.98 467.42
70.31 444.14
74.05 457.17
75.42 467.87
82.25 442.04
67.95 471.36
100.09 460.7
47.28 431.33
72.41 432.6
77.67 447.61
63.7 443.87
79.77 446.87
93.84 465.74
84.95 447.86
70.16 447.65
84.24 437.87
73.32 483.51
86.17 479.65
65.43 455.16
94.59 431.91
86.8 470.68
58.18 429.28
89.66 450.81
87.39 437.73
36.35 460.21
79.62 442.86
50.52 482.99
51.96 440.0
74.73 478.48
78.33 455.28
85.19 436.94
83.13 461.06
53.49 438.28
88.86 472.61
60.89 426.85
61.14 470.18
68.29 455.38
57.62 428.32
83.63 480.35
78.1 455.56
66.34 447.66
79.02 443.06
68.96 452.43
71.13 477.81
87.01 431.66
74.3 431.8
77.62 446.67
59.56 445.26
41.66 425.72
73.27 430.58
77.16 439.86
67.02 441.11
52.8 434.72
39.04 434.01
65.47 475.64
74.32 460.44
69.22 436.4
93.88 461.03
69.83 479.08
84.11 435.76
78.65 460.14
69.31 442.2
70.3 447.69
68.23 431.15
71.76 445.0
85.88 431.59
71.09 467.22
52.67 445.33
89.68 470.57
73.66 473.77
58.94 447.67
87.05 474.29
67.0 437.14
43.18 432.56
80.62 459.14
59.72 446.19
72.1 428.1
69.15 468.46
55.66 435.02
61.19 445.52
74.62 462.69
73.35 455.75
68.85 463.74
39.89 439.79
53.16 443.26
52.97 432.04
79.87 465.86
84.09 465.6
100.15 469.43
79.77 440.75
88.99 481.32
76.14 479.87
69.13 458.59
93.03 438.62
77.92 445.59
74.89 481.87
88.7 475.01
62.94 436.54
89.62 456.63
81.04 451.69
94.53 463.04
64.02 446.1
70.57 438.67
70.32 466.88
84.86 444.6
81.41 440.26
89.45 483.92
82.71 475.19
93.93 479.24
70.6 434.92
87.68 454.16
87.58 447.58
74.4 467.9
67.35 426.29
63.61 447.02
76.89 455.85
78.08 476.46
69.17 437.48
53.31 452.77
93.32 491.54
42.47 438.41
82.58 476.1
94.59 464.58
86.31 467.74
72.57 442.12
80.76 453.34
71.93 425.29
47.54 449.63
95.72 462.88
77.03 464.67
80.49 489.96
77.67 482.38
78.72 437.95
58.77 429.2
74.8 453.34
51.34 442.47
90.41 462.6
91.1 478.79
62.57 456.11
84.27 450.33
42.93 434.83
40.96 433.43
76.53 456.02
69.74 485.23
74.99 473.57
70.45 469.94
91.49 452.07
88.97 475.32
89.13 480.69
46.52 444.01
60.55 465.17
88.71 480.61
89.15 476.04
83.02 441.76
75.19 428.24
87.35 444.77
85.66 463.1
91.66 470.5
63.47 431.0
72.25 430.68
70.58 436.42
60.1 452.33
89.29 440.16
67.43 435.75
67.58 449.74
70.8 430.73
63.62 432.75
66.68 446.79
90.76 486.35
75.34 453.18
59.77 458.31
80.79 480.26
74.1 448.65
41.34 458.41
58.78 435.39
97.78 450.21
74.85 459.59
69.84 445.84
75.36 441.08
85.8 467.33
90.11 444.19
61.63 432.96
44.76 438.09
89.7 467.9
72.51 475.72
74.98 477.51
79.59 435.13
78.42 477.9
61.23 457.26
47.56 467.53
93.06 465.15
89.65 474.28
50.5 444.49
44.84 452.84
85.32 435.38
82.94 433.57
67.26 435.27
79.05 468.49
62.03 433.07
94.36 430.63
60.02 440.74
95.46 474.49
84.92 449.74
62.8 436.73
90.81 434.58
80.9 473.93
55.84 435.99
75.3 466.83
37.34 427.22
79.5 444.07
87.29 469.57
81.5 459.89
76.42 479.59
75.75 440.92
84.95 480.87
62.37 441.9
49.25 430.2
74.16 465.16
90.55 471.32
94.28 485.43
63.31 495.35
78.9 449.12
90.97 480.53
87.34 457.07
80.8 443.67
90.95 477.52
69.97 472.95
89.45 472.54
76.08 469.17
83.35 435.21
92.16 477.78
58.42 475.89
85.06 483.9
88.91 476.2
83.33 462.16
88.49 471.05
96.88 484.71
73.86 446.34
65.17 469.02
69.41 432.12
81.23 467.28
54.07 429.66
89.17 469.49
78.85 485.87
84.44 481.95
83.02 479.03
90.66 434.5
75.29 464.9
82.6 452.71
45.4 429.74
66.33 457.09
45.33 446.77
67.12 460.76
84.14 471.95
80.81 453.29
49.13 441.61
87.29 464.73
52.95 464.68
68.92 430.59
85.21 438.01
88.56 479.08
55.09 436.39
48.64 447.07
87.85 479.91
87.42 489.05
62.75 463.17
94.86 471.26
63.54 480.49
69.46 473.78
74.66 455.5
80.57 446.27
84.7 482.2
72.6 452.48
52.63 464.48
82.01 438.1
75.22 445.6
51.05 442.43
80.1 436.67
81.58 466.56
65.94 457.29
86.74 487.03
62.57 464.93
57.37 466.0
88.91 469.52
72.26 428.88
74.98 474.3
71.19 461.06
62.82 465.57
55.31 467.67
71.57 466.99
59.16 463.72
40.8 443.78
67.63 445.23
97.2 464.43
91.16 484.36
64.81 442.16
85.61 464.11
93.42 462.48
77.36 477.49
67.03 437.04
89.45 457.09
54.84 450.6
53.48 465.78
54.47 427.1
43.36 459.81
48.92 447.36
81.22 488.92
60.35 433.36
75.98 483.35
74.24 469.53
85.21 476.96
68.46 440.75
78.31 462.55
89.25 448.04
68.57 455.24
67.38 494.75
53.6 444.58
91.69 484.82
78.21 442.9
94.19 485.46
37.19 457.81
83.53 481.92
79.45 443.23
99.9 474.29
50.39 430.46
74.33 455.71
83.66 438.34
89.48 485.83
64.59 452.82
75.68 435.04
64.18 451.21
70.09 465.81
70.58 458.42
99.28 470.22
81.89 449.24
87.99 471.43
88.21 473.26
91.29 452.82
52.72 432.69
67.5 444.13
92.05 467.21
63.23 445.98
90.88 436.91
74.91 455.01
85.39 437.11
96.98 477.06
56.28 441.71
65.62 495.76
55.32 445.63
88.88 464.72
39.49 438.03
54.59 434.78
57.19 444.67
45.06 452.24
40.62 450.92
90.21 436.53
90.91 435.53
74.96 440.01
54.21 443.1
63.62 427.49
50.04 436.25
51.23 440.74
82.71 443.54
92.04 459.42
90.67 439.66
91.14 464.15
70.43 459.1
66.63 455.68
86.95 469.08
96.69 478.02
70.88 456.8
47.14 441.13
63.36 463.88
60.58 430.45
54.3 449.18
65.09 447.89
48.16 431.59
81.51 447.5
68.57 475.58
73.16 453.24
80.88 446.4
85.4 476.81
75.77 474.1
75.76 450.71
70.21 433.62
55.53 465.14
80.48 445.18
72.41 474.12
83.25 483.91
82.12 486.68
94.75 464.98
95.79 481.4
86.02 479.2
78.29 463.86
82.23 472.3
52.86 446.51
60.66 437.71
62.77 458.94
65.54 437.91
95.4 490.76
51.78 439.66
63.62 463.27
83.09 473.99
61.81 433.38
68.24 459.01
72.41 471.44
79.64 471.91
66.95 465.15
91.98 446.66
64.96 438.15
88.93 447.14
85.62 472.32
84.0 441.68
89.41 440.04
67.4 444.82
76.75 457.26
89.06 428.83
64.02 449.07
64.12 435.21
81.22 471.03
100.13 465.56
58.07 442.83
63.42 460.3
83.32 474.25
74.28 477.97
71.91 472.16
85.8 456.08
55.58 452.41
69.7 463.71
63.79 433.72
86.59 456.4
48.28 448.43
80.42 481.6
98.58 457.07
72.83 451.0
56.07 440.28
67.13 437.47
84.49 443.57
68.37 426.6
86.07 470.87
83.82 478.37
74.86 453.92
53.52 470.22
80.61 434.54
43.56 442.89
89.35 479.03
97.28 476.06
80.49 473.88
88.9 451.75
49.7 439.2
68.64 439.7
98.58 463.6
82.12 447.47
78.32 447.92
86.04 471.08
91.36 437.55
81.02 448.27
76.05 431.69
71.81 449.09
82.13 448.79
74.27 460.21
71.32 479.28
74.59 483.11
84.44 450.75
43.39 437.97
87.2 459.76
77.11 457.75
82.81 469.33
85.67 433.28
95.58 444.64
74.46 463.1
90.02 460.91
81.93 479.35
86.29 449.23
85.43 474.51
71.66 435.02
71.33 435.45
67.72 452.38
84.23 480.41
74.44 478.96
71.48 468.87
56.3 434.01
68.13 466.36
68.35 435.28
85.23 486.46
79.78 468.19
98.98 468.37
72.87 474.19
90.93 440.32
72.94 485.32
72.3 464.27
77.74 479.25
78.29 430.4
69.1 447.49
55.79 438.23
75.09 492.09
88.98 475.36
64.26 452.56
25.89 427.84
59.18 433.95
67.48 435.27
89.85 454.62
50.62 472.17
83.97 452.42
96.51 472.17
80.09 481.83
84.02 458.78
61.97 447.5
88.51 463.4
81.83 473.57
90.63 433.72
73.37 431.85
77.5 433.47
57.46 432.84
51.91 436.6
79.14 490.23
69.24 477.16
41.85 441.06
95.1 440.86
74.53 477.94
64.85 474.47
57.07 470.67
62.9 447.31
72.21 466.8
65.99 430.91
73.67 434.75
88.59 469.52
61.19 438.9
49.37 429.56
48.65 432.92
56.18 442.87
71.56 466.59
87.46 479.61
70.02 471.08
61.2 433.37
60.54 443.92
71.82 443.5
44.8 439.89
67.32 434.66
78.77 487.57
96.02 464.64
90.56 470.92
83.19 444.39
68.45 442.48
99.19 449.61
88.11 435.02
79.29 458.67
87.76 461.74
82.56 438.31
79.24 462.38
67.24 460.56
58.98 439.22
84.22 444.64
89.12 430.34
50.07 430.46
98.86 456.79
95.79 468.82
70.06 448.51
41.71 470.77
86.55 465.74
71.97 430.21
96.4 449.23
91.73 461.89
91.62 445.72
59.14 466.13
92.56 448.71
83.71 469.25
55.43 450.56
74.91 464.46
89.41 471.13
69.81 461.52
86.01 451.09
86.05 431.51
80.98 469.8
63.62 442.28
64.16 458.67
75.63 462.4
80.21 453.54
59.7 444.38
47.6 440.52
63.78 433.62
89.37 481.96
70.55 452.75
84.42 481.28
80.62 439.03
52.56 435.75
83.26 436.03
70.64 445.6
89.95 462.65
51.53 438.66
84.83 447.32
59.68 484.55
97.88 476.8
85.03 480.34
47.38 440.63
48.08 459.48
79.65 490.78
83.39 483.56
82.18 429.38
51.71 440.27
77.33 445.34
72.81 447.43
84.26 439.91
73.23 459.27
79.73 478.89
62.32 466.7
78.58 463.5
79.88 436.21
65.87 443.94
80.28 439.63
71.33 460.95
70.33 448.69
57.73 444.63
99.46 473.51
68.61 462.56
95.91 451.76
80.42 491.81
51.01 429.52
74.08 437.9
80.73 467.54
54.71 449.97
73.75 436.62
88.43 477.68
74.66 447.26
70.04 439.76
74.64 437.49
85.11 455.14
82.97 485.5
55.59 444.1
49.9 432.33
89.99 471.23
91.61 463.89
82.95 445.54
92.62 446.09
59.64 445.12
63.0 443.31
85.38 484.16
85.23 477.76
52.08 430.28
38.05 446.48
71.98 481.03
90.66 466.07
83.14 447.47
65.22 455.93
91.67 479.62
66.04 455.06
78.19 475.06
54.47 438.89
67.26 432.7
72.56 452.6
82.15 451.75
86.32 430.66
88.17 491.9
72.78 439.82
69.58 460.73
69.86 449.7
65.37 439.42
52.37 439.84
79.63 485.86
83.14 458.1
88.25 479.92
55.85 458.29
52.55 489.45
62.74 434.0
90.08 431.24
54.5 439.5
49.88 467.46
48.96 429.27
86.77 452.1
96.66 472.41
70.84 442.14
83.83 441.0
68.22 463.07
82.22 445.71
87.9 483.16
66.83 440.45
85.36 481.83
60.9 467.6
83.51 450.88
52.96 425.5
73.02 451.87
43.11 428.94
61.41 439.86
65.32 433.44
93.68 438.23
42.39 436.95
68.18 470.19
89.18 484.66
64.79 430.81
43.48 433.37
80.4 453.02
72.43 453.5
80.0 463.09
45.65 464.56
63.02 452.12
74.39 470.9
57.77 450.89
76.8 445.04
67.39 444.72
73.96 460.38
72.75 446.8
71.69 465.05
84.98 484.13
87.68 488.27
50.36 447.09
68.11 452.02
66.27 455.55
89.72 480.99
61.07 467.68
RH PE
73.17 463.26
59.08 444.37
92.14 488.56
76.64 446.48
96.62 473.9
58.77 443.67
75.24 467.35
66.43 478.42
41.25 475.98
70.72 477.5
75.04 453.02
64.22 453.99
84.15 440.29
61.83 451.28
87.59 433.99
43.08 462.19
48.84 467.54
77.51 477.2
63.59 459.85
55.28 464.3
66.26 468.27
64.77 495.24
83.31 483.8
47.19 443.61
54.93 436.06
74.62 443.25
72.52 464.16
88.44 475.52
92.28 484.41
41.85 437.89
44.28 445.11
64.58 438.86
63.25 440.98
78.61 436.65
44.51 444.26
89.46 465.86
74.52 444.37
88.86 450.69
75.51 469.02
78.64 448.86
76.65 447.14
80.44 469.18
79.89 482.8
88.28 476.7
84.6 474.99
42.69 444.22
78.41 461.33
61.07 448.06
50.0 474.6
77.29 473.05
43.66 432.06
83.8 467.41
66.47 430.12
93.09 473.62
80.52 471.81
68.99 442.99
57.27 442.77
95.53 491.49
71.72 447.46
57.88 446.11
63.34 442.44
48.07 446.22
91.87 471.49
87.27 463.5
64.4 440.01
43.4 441.03
72.24 452.68
90.22 474.91
74.0 478.77
71.85 434.2
86.62 437.91
97.41 477.61
84.44 431.65
81.55 430.57
75.66 481.09
79.41 445.56
58.91 475.74
90.06 435.12
79.0 446.15
69.47 436.64
51.47 436.69
83.13 468.75
40.33 466.6
81.69 465.48
94.55 441.34
91.81 441.83
63.62 464.7
49.35 437.99
69.61 459.12
38.75 429.69
90.17 459.8
81.24 433.63
48.46 442.84
76.72 485.13
51.16 459.12
76.34 445.31
67.3 480.8
52.38 432.55
76.44 443.86
91.55 449.77
71.9 470.71
80.05 452.17
63.77 478.29
62.26 428.54
89.04 478.27
58.02 439.58
81.82 457.32
91.14 475.51
88.92 439.66
84.83 471.99
91.76 479.81
86.56 434.78
57.21 446.58
54.25 437.76
63.8 459.36
33.71 462.28
67.25 464.33
60.11 444.36
74.55 438.64
67.34 470.49
42.75 455.13
55.2 450.22
83.61 440.43
88.78 482.98
100.12 460.44
64.52 444.97
51.41 433.94
85.78 439.73
75.41 434.48
81.63 442.33
51.92 457.67
70.12 454.66
53.83 432.21
77.23 457.66
65.67 435.21
71.18 448.22
81.96 475.51
79.54 446.53
47.09 441.3
57.69 433.54
78.89 472.52
85.29 474.77
40.13 435.1
77.06 450.74
67.38 442.7
62.44 426.56
77.43 463.71
58.77 447.06
67.72 452.27
42.14 445.78
84.16 438.65
89.79 480.15
67.21 447.19
72.14 443.04
97.49 488.81
87.74 455.75
96.3 455.86
61.25 457.68
88.38 479.11
74.77 432.84
68.18 448.37
77.2 447.06
49.54 443.53
92.22 445.21
33.65 441.7
64.59 450.93
100.09 451.44
68.04 441.29
48.94 458.85
74.47 481.46
81.02 467.19
71.17 461.54
53.85 439.08
70.67 467.22
59.36 468.8
57.17 426.93
70.29 474.65
83.37 468.97
87.36 433.97
100.09 450.53
68.78 444.51
70.98 469.03
75.68 466.56
47.49 457.57
71.99 440.13
66.55 433.24
74.73 452.55
64.78 443.29
75.13 431.76
56.38 454.97
94.35 456.7
86.55 486.03
82.95 472.79
88.42 452.03
85.61 443.41
58.39 441.93
74.28 432.64
87.85 480.25
83.5 466.68
65.24 494.39
75.01 454.72
84.52 448.71
80.52 469.76
75.14 450.71
75.75 444.01
76.72 453.2
85.47 450.87
57.95 441.73
78.32 465.09
52.2 447.28
93.69 491.16
75.74 450.98
67.56 446.3
69.46 436.48
74.58 460.84
53.23 442.56
88.72 467.3
96.16 479.13
68.26 441.15
86.39 445.52
85.34 475.4
72.64 469.3
97.82 463.57
77.22 445.32
80.59 461.03
46.91 466.74
57.76 444.04
53.09 434.01
84.31 465.23
71.58 440.6
92.97 466.74
74.55 433.48
78.96 473.59
64.44 474.81
68.23 454.75
70.81 452.94
61.66 435.83
77.76 482.19
69.49 466.66
96.26 462.59
55.74 447.82
95.61 462.73
84.75 447.98
75.3 462.72
67.5 442.42
80.92 444.69
79.23 466.7
81.1 453.84
32.8 436.92
84.31 486.37
46.15 440.43
53.96 446.82
59.83 484.91
75.3 437.76
42.53 438.91
70.58 464.19
91.69 442.19
63.55 446.86
61.51 457.15
69.55 482.57
98.08 476.03
79.34 428.89
81.28 472.7
78.99 445.6
80.38 464.78
51.16 440.42
72.17 428.41
75.39 438.5
68.91 438.28
96.38 476.29
70.54 448.46
45.8 438.99
57.95 471.8
81.89 471.81
69.32 449.82
59.14 442.14
81.54 441.46
85.81 477.62
65.41 446.76
81.15 472.52
95.87 471.58
90.24 440.85
75.13 431.37
88.22 437.33
81.48 469.22
89.84 471.11
43.57 439.17
63.16 445.33
57.14 473.71
77.76 452.66
90.56 440.99
60.98 467.42
70.31 444.14
74.05 457.17
75.42 467.87
82.25 442.04
67.95 471.36
100.09 460.7
47.28 431.33
72.41 432.6
77.67 447.61
63.7 443.87
79.77 446.87
93.84 465.74
84.95 447.86
70.16 447.65
84.24 437.87
73.32 483.51
86.17 479.65
65.43 455.16
94.59 431.91
86.8 470.68
58.18 429.28
89.66 450.81
87.39 437.73
36.35 460.21
79.62 442.86
50.52 482.99
51.96 440.0
74.73 478.48
78.33 455.28
85.19 436.94
83.13 461.06
53.49 438.28
88.86 472.61
60.89 426.85
61.14 470.18
68.29 455.38
57.62 428.32
83.63 480.35
78.1 455.56
66.34 447.66
79.02 443.06
68.96 452.43
71.13 477.81
87.01 431.66
74.3 431.8
77.62 446.67
59.56 445.26
41.66 425.72
73.27 430.58
77.16 439.86
67.02 441.11
52.8 434.72
39.04 434.01
65.47 475.64
74.32 460.44
69.22 436.4
93.88 461.03
69.83 479.08
84.11 435.76
78.65 460.14
69.31 442.2
70.3 447.69
68.23 431.15
71.76 445.0
85.88 431.59
71.09 467.22
52.67 445.33
89.68 470.57
73.66 473.77
58.94 447.67
87.05 474.29
67.0 437.14
43.18 432.56
80.62 459.14
59.72 446.19
72.1 428.1
69.15 468.46
55.66 435.02
61.19 445.52
74.62 462.69
73.35 455.75
68.85 463.74
39.89 439.79
53.16 443.26
52.97 432.04
79.87 465.86
84.09 465.6
100.15 469.43
79.77 440.75
88.99 481.32
76.14 479.87
69.13 458.59
93.03 438.62
77.92 445.59
74.89 481.87
88.7 475.01
62.94 436.54
89.62 456.63
81.04 451.69
94.53 463.04
64.02 446.1
70.57 438.67
70.32 466.88
84.86 444.6
81.41 440.26
89.45 483.92
82.71 475.19
93.93 479.24
70.6 434.92
87.68 454.16
87.58 447.58
74.4 467.9
67.35 426.29
63.61 447.02
76.89 455.85
78.08 476.46
69.17 437.48
53.31 452.77
93.32 491.54
42.47 438.41
82.58 476.1
94.59 464.58
86.31 467.74
72.57 442.12
80.76 453.34
71.93 425.29
47.54 449.63
95.72 462.88
77.03 464.67
80.49 489.96
77.67 482.38
78.72 437.95
58.77 429.2
74.8 453.34
51.34 442.47
90.41 462.6
91.1 478.79
62.57 456.11
84.27 450.33
42.93 434.83
40.96 433.43
76.53 456.02
69.74 485.23
74.99 473.57
70.45 469.94
91.49 452.07
88.97 475.32
89.13 480.69
46.52 444.01
60.55 465.17
88.71 480.61
89.15 476.04
83.02 441.76
75.19 428.24
87.35 444.77
85.66 463.1
91.66 470.5
63.47 431.0
72.25 430.68
70.58 436.42
60.1 452.33
89.29 440.16
67.43 435.75
67.58 449.74
70.8 430.73
63.62 432.75
66.68 446.79
90.76 486.35
75.34 453.18
59.77 458.31
80.79 480.26
74.1 448.65
41.34 458.41
58.78 435.39
97.78 450.21
74.85 459.59
69.84 445.84
75.36 441.08
85.8 467.33
90.11 444.19
61.63 432.96
44.76 438.09
89.7 467.9
72.51 475.72
74.98 477.51
79.59 435.13
78.42 477.9
61.23 457.26
47.56 467.53
93.06 465.15
89.65 474.28
50.5 444.49
44.84 452.84
85.32 435.38
82.94 433.57
67.26 435.27
79.05 468.49
62.03 433.07
94.36 430.63
60.02 440.74
95.46 474.49
84.92 449.74
62.8 436.73
90.81 434.58
80.9 473.93
55.84 435.99
75.3 466.83
37.34 427.22
79.5 444.07
87.29 469.57
81.5 459.89
76.42 479.59
75.75 440.92
84.95 480.87
62.37 441.9
49.25 430.2
74.16 465.16
90.55 471.32
94.28 485.43
63.31 495.35
78.9 449.12
90.97 480.53
87.34 457.07
80.8 443.67
90.95 477.52
69.97 472.95
89.45 472.54
76.08 469.17
83.35 435.21
92.16 477.78
58.42 475.89
85.06 483.9
88.91 476.2
83.33 462.16
88.49 471.05
96.88 484.71
73.86 446.34
65.17 469.02
69.41 432.12
81.23 467.28
54.07 429.66
89.17 469.49
78.85 485.87
84.44 481.95
83.02 479.03
90.66 434.5
75.29 464.9
82.6 452.71
45.4 429.74
66.33 457.09
45.33 446.77
67.12 460.76
84.14 471.95
80.81 453.29
49.13 441.61
87.29 464.73
52.95 464.68
68.92 430.59
85.21 438.01
88.56 479.08
55.09 436.39
48.64 447.07
87.85 479.91
87.42 489.05
62.75 463.17
94.86 471.26
63.54 480.49
69.46 473.78
74.66 455.5
80.57 446.27
84.7 482.2
72.6 452.48
52.63 464.48
82.01 438.1
75.22 445.6
51.05 442.43
80.1 436.67
81.58 466.56
65.94 457.29
86.74 487.03
62.57 464.93
57.37 466.0
88.91 469.52
72.26 428.88
74.98 474.3
71.19 461.06
62.82 465.57
55.31 467.67
71.57 466.99
59.16 463.72
40.8 443.78
67.63 445.23
97.2 464.43
91.16 484.36
64.81 442.16
85.61 464.11
93.42 462.48
77.36 477.49
67.03 437.04
89.45 457.09
54.84 450.6
53.48 465.78
54.47 427.1
43.36 459.81
48.92 447.36
81.22 488.92
60.35 433.36
75.98 483.35
74.24 469.53
85.21 476.96
68.46 440.75
78.31 462.55
89.25 448.04
68.57 455.24
67.38 494.75
53.6 444.58
91.69 484.82
78.21 442.9
94.19 485.46
37.19 457.81
83.53 481.92
79.45 443.23
99.9 474.29
50.39 430.46
74.33 455.71
83.66 438.34
89.48 485.83
64.59 452.82
75.68 435.04
64.18 451.21
70.09 465.81
70.58 458.42
99.28 470.22
81.89 449.24
87.99 471.43
88.21 473.26
91.29 452.82
52.72 432.69
67.5 444.13
92.05 467.21
63.23 445.98
90.88 436.91
74.91 455.01
85.39 437.11
96.98 477.06
56.28 441.71
65.62 495.76
55.32 445.63
88.88 464.72
39.49 438.03
54.59 434.78
57.19 444.67
45.06 452.24
40.62 450.92
90.21 436.53
90.91 435.53
74.96 440.01
54.21 443.1
63.62 427.49
50.04 436.25
51.23 440.74
82.71 443.54
92.04 459.42
90.67 439.66
91.14 464.15
70.43 459.1
66.63 455.68
86.95 469.08
96.69 478.02
70.88 456.8
47.14 441.13
63.36 463.88
60.58 430.45
54.3 449.18
65.09 447.89
48.16 431.59
81.51 447.5
68.57 475.58
73.16 453.24
80.88 446.4
85.4 476.81
75.77 474.1
75.76 450.71
70.21 433.62
55.53 465.14
80.48 445.18
72.41 474.12
83.25 483.91
82.12 486.68
94.75 464.98
95.79 481.4
86.02 479.2
78.29 463.86
82.23 472.3
52.86 446.51
60.66 437.71
62.77 458.94
65.54 437.91
95.4 490.76
51.78 439.66
63.62 463.27
83.09 473.99
61.81 433.38
68.24 459.01
72.41 471.44
79.64 471.91
66.95 465.15
91.98 446.66
64.96 438.15
88.93 447.14
85.62 472.32
84.0 441.68
89.41 440.04
67.4 444.82
76.75 457.26
89.06 428.83
64.02 449.07
64.12 435.21
81.22 471.03
100.13 465.56
58.07 442.83
63.42 460.3
83.32 474.25
74.28 477.97
71.91 472.16
85.8 456.08
55.58 452.41
69.7 463.71
63.79 433.72
86.59 456.4
48.28 448.43
80.42 481.6
98.58 457.07
72.83 451.0
56.07 440.28
67.13 437.47
84.49 443.57
68.37 426.6
86.07 470.87
83.82 478.37
74.86 453.92
53.52 470.22
80.61 434.54
43.56 442.89
89.35 479.03
97.28 476.06
80.49 473.88
88.9 451.75
49.7 439.2
68.64 439.7
98.58 463.6
82.12 447.47
78.32 447.92
86.04 471.08
91.36 437.55
81.02 448.27
76.05 431.69
71.81 449.09
82.13 448.79
74.27 460.21
71.32 479.28
74.59 483.11
84.44 450.75
43.39 437.97
87.2 459.76
77.11 457.75
82.81 469.33
85.67 433.28
95.58 444.64
74.46 463.1
90.02 460.91
81.93 479.35
86.29 449.23
85.43 474.51
71.66 435.02
71.33 435.45
67.72 452.38
84.23 480.41
74.44 478.96
71.48 468.87
56.3 434.01
68.13 466.36
68.35 435.28
85.23 486.46
79.78 468.19
98.98 468.37
72.87 474.19
90.93 440.32
72.94 485.32
72.3 464.27
77.74 479.25
78.29 430.4
69.1 447.49
55.79 438.23
75.09 492.09
88.98 475.36
64.26 452.56
25.89 427.84
59.18 433.95
67.48 435.27
89.85 454.62
50.62 472.17
83.97 452.42
96.51 472.17
80.09 481.83
84.02 458.78
61.97 447.5
88.51 463.4
81.83 473.57
90.63 433.72
73.37 431.85
77.5 433.47
57.46 432.84
51.91 436.6
79.14 490.23
69.24 477.16
41.85 441.06
95.1 440.86
74.53 477.94
64.85 474.47
57.07 470.67
62.9 447.31
72.21 466.8
65.99 430.91
73.67 434.75
88.59 469.52
61.19 438.9
49.37 429.56
48.65 432.92
56.18 442.87
71.56 466.59
87.46 479.61
70.02 471.08
61.2 433.37
60.54 443.92
71.82 443.5
44.8 439.89
67.32 434.66
78.77 487.57
96.02 464.64
90.56 470.92
83.19 444.39
68.45 442.48
99.19 449.61
88.11 435.02
79.29 458.67
87.76 461.74
82.56 438.31
79.24 462.38
67.24 460.56
58.98 439.22
84.22 444.64
89.12 430.34
50.07 430.46
98.86 456.79
95.79 468.82
70.06 448.51
41.71 470.77
86.55 465.74
71.97 430.21
96.4 449.23
91.73 461.89
91.62 445.72
59.14 466.13
92.56 448.71
83.71 469.25
55.43 450.56
74.91 464.46
89.41 471.13
69.81 461.52
86.01 451.09
86.05 431.51
80.98 469.8
63.62 442.28
64.16 458.67
75.63 462.4
80.21 453.54
59.7 444.38
47.6 440.52
63.78 433.62
89.37 481.96
70.55 452.75
84.42 481.28
80.62 439.03
52.56 435.75
83.26 436.03
70.64 445.6
89.95 462.65
51.53 438.66
84.83 447.32
59.68 484.55
97.88 476.8
85.03 480.34
47.38 440.63
48.08 459.48
79.65 490.78
83.39 483.56
82.18 429.38
51.71 440.27
77.33 445.34
72.81 447.43
84.26 439.91
73.23 459.27
79.73 478.89
62.32 466.7
78.58 463.5
79.88 436.21
65.87 443.94
80.28 439.63
71.33 460.95
70.33 448.69
57.73 444.63
99.46 473.51
68.61 462.56
95.91 451.76
80.42 491.81
51.01 429.52
74.08 437.9
80.73 467.54
54.71 449.97
73.75 436.62
88.43 477.68
74.66 447.26
70.04 439.76
74.64 437.49
85.11 455.14
82.97 485.5
55.59 444.1
49.9 432.33
89.99 471.23
91.61 463.89
82.95 445.54
92.62 446.09
59.64 445.12
63.0 443.31
85.38 484.16
85.23 477.76
52.08 430.28
38.05 446.48
71.98 481.03
90.66 466.07
83.14 447.47
65.22 455.93
91.67 479.62
66.04 455.06
78.19 475.06
54.47 438.89
67.26 432.7
72.56 452.6
82.15 451.75
86.32 430.66
88.17 491.9
72.78 439.82
69.58 460.73
69.86 449.7
65.37 439.42
52.37 439.84
79.63 485.86
83.14 458.1
88.25 479.92
55.85 458.29
52.55 489.45
62.74 434.0
90.08 431.24
54.5 439.5
49.88 467.46
48.96 429.27
86.77 452.1
96.66 472.41
70.84 442.14
83.83 441.0
68.22 463.07
82.22 445.71
87.9 483.16
66.83 440.45
85.36 481.83
60.9 467.6
83.51 450.88
52.96 425.5
73.02 451.87
43.11 428.94
61.41 439.86
65.32 433.44
93.68 438.23
42.39 436.95
68.18 470.19
89.18 484.66
64.79 430.81
43.48 433.37
80.4 453.02
72.43 453.5
80.0 463.09
45.65 464.56
63.02 452.12
74.39 470.9
57.77 450.89
76.8 445.04
67.39 444.72
73.96 460.38
72.75 446.8
71.69 465.05
84.98 484.13
87.68 488.27
50.36 447.09
68.11 452.02
66.27 455.55
89.72 480.99
61.07 467.68
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
10.82 37.5 1009.23 96.62 473.9
26.27 59.44 1012.23 58.77 443.67
15.89 43.96 1014.02 75.24 467.35
9.48 44.71 1019.12 66.43 478.42
14.64 45.0 1021.78 41.25 475.98
11.74 43.56 1015.14 70.72 477.5
17.99 43.72 1008.64 75.04 453.02
20.14 46.93 1014.66 64.22 453.99
24.34 73.5 1011.31 84.15 440.29
25.71 58.59 1012.77 61.83 451.28
26.19 69.34 1009.48 87.59 433.99
21.42 43.79 1015.76 43.08 462.19
18.21 45.0 1022.86 48.84 467.54
11.04 41.74 1022.6 77.51 477.2
14.45 52.75 1023.97 63.59 459.85
13.97 38.47 1015.15 55.28 464.3
17.76 42.42 1009.09 66.26 468.27
5.41 40.07 1019.16 64.77 495.24
7.76 42.28 1008.52 83.31 483.8
27.23 63.9 1014.3 47.19 443.61
27.36 48.6 1003.18 54.93 436.06
27.47 70.72 1009.97 74.62 443.25
14.6 39.31 1011.11 72.52 464.16
7.91 39.96 1023.57 88.44 475.52
5.81 35.79 1012.14 92.28 484.41
30.53 65.18 1012.69 41.85 437.89
23.87 63.94 1019.02 44.28 445.11
26.09 58.41 1013.64 64.58 438.86
29.27 66.85 1011.11 63.25 440.98
27.38 74.16 1010.08 78.61 436.65
24.81 63.94 1018.76 44.51 444.26
12.75 44.03 1007.29 89.46 465.86
24.66 63.73 1011.4 74.52 444.37
16.38 47.45 1010.08 88.86 450.69
13.91 39.35 1014.69 75.51 469.02
23.18 51.3 1012.04 78.64 448.86
22.47 47.45 1007.62 76.65 447.14
13.39 44.85 1017.24 80.44 469.18
9.28 41.54 1018.33 79.89 482.8
11.82 42.86 1014.12 88.28 476.7
10.27 40.64 1020.63 84.6 474.99
22.92 63.94 1019.28 42.69 444.22
16.0 37.87 1020.24 78.41 461.33
21.22 43.43 1010.96 61.07 448.06
13.46 44.71 1014.51 50.0 474.6
9.39 40.11 1029.14 77.29 473.05
31.07 73.5 1010.58 43.66 432.06
12.82 38.62 1018.71 83.8 467.41
32.57 78.92 1011.6 66.47 430.12
8.11 42.18 1014.82 93.09 473.62
13.92 39.39 1012.94 80.52 471.81
23.04 59.43 1010.23 68.99 442.99
27.31 64.44 1014.65 57.27 442.77
5.91 39.33 1010.18 95.53 491.49
25.26 61.08 1013.68 71.72 447.46
27.97 58.84 1002.25 57.88 446.11
26.08 52.3 1007.03 63.34 442.44
29.01 65.71 1013.61 48.07 446.22
12.18 40.1 1016.67 91.87 471.49
13.76 45.87 1008.89 87.27 463.5
25.5 58.79 1016.02 64.4 440.01
28.26 65.34 1014.56 43.4 441.03
21.39 62.96 1019.49 72.24 452.68
7.26 40.69 1020.43 90.22 474.91
10.54 34.03 1018.71 74.0 478.77
27.71 74.34 998.14 71.85 434.2
23.11 68.3 1017.83 86.62 437.91
7.51 41.01 1024.61 97.41 477.61
26.46 74.67 1016.65 84.44 431.65
29.34 74.34 998.58 81.55 430.57
10.32 42.28 1008.82 75.66 481.09
22.74 61.02 1009.56 79.41 445.56
13.48 39.85 1012.71 58.91 475.74
25.52 69.75 1010.36 90.06 435.12
21.58 67.25 1017.39 79.0 446.15
27.66 76.86 1001.31 69.47 436.64
26.96 69.45 1013.89 51.47 436.69
12.29 42.18 1016.53 83.13 468.75
15.86 43.02 1012.18 40.33 466.6
13.87 45.08 1024.42 81.69 465.48
24.09 73.68 1014.93 94.55 441.34
20.45 69.45 1012.53 91.81 441.83
15.07 39.3 1019.0 63.62 464.7
32.72 69.75 1009.6 49.35 437.99
18.23 58.96 1015.55 69.61 459.12
35.56 68.94 1006.56 38.75 429.69
18.36 51.43 1010.57 90.17 459.8
26.35 64.05 1009.81 81.24 433.63
25.92 60.95 1014.62 48.46 442.84
8.01 41.66 1014.49 76.72 485.13
19.63 52.72 1025.09 51.16 459.12
20.02 67.32 1012.05 76.34 445.31
10.08 40.72 1022.7 67.3 480.8
27.23 66.48 1005.23 52.38 432.55
23.37 63.77 1013.42 76.44 443.86
18.74 59.21 1018.3 91.55 449.77
14.81 43.69 1017.19 71.9 470.71
23.1 51.3 1011.93 80.05 452.17
10.72 41.38 1021.6 63.77 478.29
29.46 71.94 1006.96 62.26 428.54
8.1 40.64 1020.66 89.04 478.27
27.29 62.66 1007.63 58.02 439.58
17.1 49.69 1005.53 81.82 457.32
11.49 44.2 1018.79 91.14 475.51
23.69 65.59 1010.85 88.92 439.66
13.51 40.89 1011.03 84.83 471.99
9.64 39.35 1015.1 91.76 479.81
25.65 78.92 1010.83 86.56 434.78
21.59 61.87 1011.18 57.21 446.58
27.98 58.33 1013.92 54.25 437.76
18.8 39.72 1001.24 63.8 459.36
18.28 44.71 1016.99 33.71 462.28
13.55 43.48 1016.08 67.25 464.33
22.99 46.21 1010.71 60.11 444.36
23.94 59.39 1014.32 74.55 438.64
13.74 34.03 1018.69 67.34 470.49
21.3 41.1 1001.86 42.75 455.13
27.54 66.93 1017.06 55.2 450.22
24.81 63.73 1009.34 83.61 440.43
4.97 42.85 1014.02 88.78 482.98
15.22 50.88 1014.19 100.12 460.44
23.88 54.2 1012.81 64.52 444.97
33.01 68.67 1005.2 51.41 433.94
25.98 73.18 1012.28 85.78 439.73
28.18 73.88 1005.89 75.41 434.48
21.67 60.84 1017.93 81.63 442.33
17.67 45.09 1014.26 51.92 457.67
21.37 57.76 1018.8 70.12 454.66
28.69 67.25 1017.71 53.83 432.21
16.61 43.77 1012.25 77.23 457.66
27.91 63.76 1010.27 65.67 435.21
20.97 47.43 1007.64 71.18 448.22
10.8 41.66 1013.79 81.96 475.51
20.61 62.91 1013.24 79.54 446.53
25.45 57.32 1011.7 47.09 441.3
30.16 69.34 1007.67 57.69 433.54
4.99 39.04 1020.45 78.89 472.52
10.51 44.78 1012.59 85.29 474.77
33.79 69.05 1001.62 40.13 435.1
21.34 59.8 1016.92 77.06 450.74
23.4 65.06 1014.32 67.38 442.7
32.21 68.14 1003.34 62.44 426.56
14.26 42.32 1016.0 77.43 463.71
27.71 66.93 1016.85 58.77 447.06
21.95 57.76 1018.02 67.72 452.27
25.76 63.94 1018.49 42.14 445.78
23.68 68.3 1017.93 84.16 438.65
8.28 40.77 1011.55 89.79 480.15
23.44 62.52 1016.46 67.21 447.19
25.32 48.41 1008.47 72.14 443.04
3.94 39.9 1008.06 97.49 488.81
17.3 57.76 1016.26 87.74 455.75
18.2 49.39 1018.83 96.3 455.86
21.43 46.97 1013.94 61.25 457.68
11.16 40.05 1014.95 88.38 479.11
30.38 74.16 1007.44 74.77 432.84
23.36 62.52 1016.18 68.18 448.37
21.69 47.45 1007.56 77.2 447.06
23.62 49.21 1014.1 49.54 443.53
21.87 61.45 1011.13 92.22 445.21
29.25 66.51 1015.53 33.65 441.7
20.03 66.86 1013.05 64.59 450.93
18.14 49.78 1002.95 100.09 451.44
24.23 56.89 1012.32 68.04 441.29
18.11 44.85 1014.48 48.94 458.85
6.57 43.65 1018.24 74.47 481.46
12.56 43.41 1016.93 81.02 467.19
13.4 41.58 1020.5 71.17 461.54
27.1 52.84 1006.28 53.85 439.08
14.28 42.74 1028.79 70.67 467.22
16.29 44.34 1019.49 59.36 468.8
31.24 71.98 1004.66 57.17 426.93
10.57 37.73 1024.36 70.29 474.65
13.8 44.21 1022.93 83.37 468.97
25.3 71.58 1010.18 87.36 433.97
18.06 50.16 1009.52 100.09 450.53
25.42 59.04 1011.98 68.78 444.51
15.07 40.69 1015.29 70.98 469.03
11.75 71.14 1019.36 75.68 466.56
20.23 52.05 1012.15 47.49 457.57
27.31 59.54 1006.24 71.99 440.13
28.57 69.84 1003.57 66.55 433.24
17.9 43.72 1008.64 74.73 452.55
23.83 71.37 1002.04 64.78 443.29
27.92 74.99 1005.47 75.13 431.76
17.34 44.78 1007.81 56.38 454.97
17.94 63.07 1012.42 94.35 456.7
6.4 39.9 1007.75 86.55 486.03
11.78 39.96 1011.37 82.95 472.79
20.28 57.25 1010.12 88.42 452.03
21.04 54.2 1012.26 85.61 443.41
25.11 67.32 1014.49 58.39 441.93
30.28 70.98 1007.51 74.28 432.64
8.14 36.24 1013.15 87.85 480.25
16.86 39.63 1004.47 83.5 466.68
6.25 40.07 1020.19 65.24 494.39
22.35 54.42 1012.46 75.01 454.72
17.98 56.85 1012.28 84.52 448.71
21.19 42.48 1013.43 80.52 469.76
20.94 44.89 1009.64 75.14 450.71
24.23 58.79 1009.8 75.75 444.01
19.18 58.2 1017.46 76.72 453.2
20.88 57.85 1012.39 85.47 450.87
23.67 63.86 1019.67 57.95 441.73
14.12 39.52 1018.41 78.32 465.09
25.23 64.63 1020.59 52.2 447.28
6.54 39.33 1011.54 93.69 491.16
20.08 62.52 1017.99 75.74 450.98
24.67 63.56 1013.75 67.56 446.3
27.82 79.74 1008.37 69.46 436.48
15.55 42.03 1017.41 74.58 460.84
24.26 69.51 1013.43 53.23 442.56
13.45 41.49 1020.19 88.72 467.3
11.06 40.64 1021.47 96.16 479.13
24.91 52.3 1008.72 68.26 441.15
22.39 59.04 1011.78 86.39 445.52
11.95 40.69 1015.62 85.34 475.4
14.85 40.69 1014.91 72.64 469.3
10.11 41.62 1017.17 97.82 463.57
23.67 68.67 1006.71 77.22 445.32
16.14 44.21 1020.36 80.59 461.03
15.11 43.13 1014.99 46.91 466.74
24.14 59.87 1018.47 57.76 444.04
30.08 67.25 1017.6 53.09 434.01
14.77 44.9 1020.5 84.31 465.23
27.6 69.34 1009.63 71.58 440.6
13.89 44.84 1023.66 92.97 466.74
26.85 75.6 1017.43 74.55 433.48
12.41 40.96 1023.36 78.96 473.59
13.08 41.74 1020.75 64.44 474.81
18.93 44.06 1017.58 68.23 454.75
20.5 49.69 1009.6 70.81 452.94
30.72 69.13 1009.94 61.66 435.83
7.55 39.22 1014.53 77.76 482.19
13.49 44.47 1030.46 69.49 466.66
15.62 40.12 1013.03 96.26 462.59
24.8 64.63 1020.69 55.74 447.82
10.03 41.62 1014.55 95.61 462.73
22.43 63.21 1012.06 84.75 447.98
14.95 39.31 1009.15 75.3 462.72
24.78 58.46 1016.82 67.5 442.42
23.2 48.41 1008.64 80.92 444.69
14.01 39.0 1016.73 79.23 466.7
19.4 64.63 1020.38 81.1 453.84
30.15 67.32 1013.83 32.8 436.92
6.91 36.08 1021.82 84.31 486.37
29.04 60.07 1015.42 46.15 440.43
26.02 63.07 1010.94 53.96 446.82
5.89 39.48 1005.11 59.83 484.91
26.52 71.64 1008.27 75.3 437.76
28.53 68.08 1013.27 42.53 438.91
16.59 39.54 1007.97 70.58 464.19
22.95 67.79 1009.89 91.69 442.19
23.96 47.43 1008.38 63.55 446.86
17.48 44.2 1018.89 61.51 457.15
6.69 43.65 1020.14 69.55 482.57
10.25 41.26 1007.44 98.08 476.03
28.87 72.58 1008.69 79.34 428.89
12.04 40.23 1018.07 81.28 472.7
22.58 52.3 1009.04 78.99 445.6
15.12 52.05 1014.63 80.38 464.78
25.48 58.95 1017.02 51.16 440.42
27.87 70.79 1003.96 72.17 428.41
23.72 70.47 1010.65 75.39 438.5
25.0 59.43 1007.84 68.91 438.28
8.42 40.64 1022.35 96.38 476.29
22.46 58.49 1011.5 70.54 448.46
29.92 57.19 1008.62 45.8 438.99
11.68 39.22 1017.9 57.95 471.8
14.04 42.44 1012.74 81.89 471.81
19.86 59.14 1016.12 69.32 449.82
25.99 68.08 1013.13 59.14 442.14
23.42 58.79 1009.74 81.54 441.46
10.6 40.22 1011.37 85.81 477.62
20.97 61.87 1011.45 65.41 446.76
14.14 39.82 1012.46 81.15 472.52
8.56 40.71 1021.27 95.87 471.58
24.86 72.39 1001.15 90.24 440.85
29.0 77.54 1011.33 75.13 431.37
27.59 71.97 1008.64 88.22 437.33
10.45 40.71 1015.68 81.48 469.22
8.51 40.78 1023.51 89.84 471.11
29.82 66.51 1010.98 43.57 439.17
22.56 62.26 1012.11 63.16 445.33
11.38 39.22 1018.62 57.14 473.71
20.25 57.76 1016.28 77.76 452.66
22.42 59.43 1007.12 90.56 440.99
14.85 38.91 1014.48 60.98 467.42
25.62 58.82 1010.02 70.31 444.14
19.85 56.53 1020.57 74.05 457.17
13.67 54.3 1015.92 75.42 467.87
24.39 70.72 1009.78 82.25 442.04
16.07 44.58 1019.52 67.95 471.36
11.6 39.1 1009.81 100.09 460.7
31.38 70.83 1010.35 47.28 431.33
29.91 76.86 998.59 72.41 432.6
19.67 59.39 1014.07 77.67 447.61
27.18 64.79 1016.27 63.7 443.87
21.39 52.3 1009.2 79.77 446.87
10.45 41.01 1020.57 93.84 465.74
19.46 56.89 1014.02 84.95 447.86
23.55 62.96 1020.16 70.16 447.65
23.35 63.47 1011.78 84.24 437.87
9.26 41.66 1016.87 73.32 483.51
10.3 41.46 1018.21 86.17 479.65
20.94 58.16 1016.88 65.43 455.16
23.13 71.25 1002.49 94.59 431.91
12.77 41.5 1014.13 86.8 470.68
28.29 69.13 1009.29 58.18 429.28
19.13 59.21 1018.32 89.66 450.81
24.44 73.5 1011.49 87.39 437.73
20.32 44.6 1015.16 36.35 460.21
20.54 69.05 1001.6 79.62 442.86
12.16 45.0 1021.51 50.52 482.99
28.09 65.27 1013.27 51.96 440.0
9.25 41.82 1033.25 74.73 478.48
21.75 49.82 1015.01 78.33 455.28
23.7 66.56 1002.07 85.19 436.94
16.22 37.87 1022.36 83.13 461.06
24.75 69.45 1013.97 53.49 438.28
10.48 39.58 1011.81 88.86 472.61
29.53 70.79 1003.7 60.89 426.85
12.59 39.72 1017.76 61.14 470.18
23.5 54.42 1012.31 68.29 455.38
29.01 66.56 1006.44 57.62 428.32
9.75 42.49 1010.57 83.63 480.35
19.55 56.53 1020.2 78.1 455.56
21.05 58.33 1013.14 66.34 447.66
24.72 68.67 1006.74 79.02 443.06
21.19 58.86 1014.19 68.96 452.43
10.77 41.54 1019.94 71.13 477.81
28.68 73.77 1004.72 87.01 431.66
29.87 73.91 1004.53 74.3 431.8
22.99 68.67 1006.65 77.62 446.67
24.66 60.29 1018.0 59.56 445.26
32.63 69.89 1013.85 41.66 425.72
31.38 72.29 1008.73 73.27 430.58
23.87 60.27 1018.94 77.16 439.86
25.6 59.15 1013.31 67.02 441.11
27.62 71.14 1011.6 52.8 434.72
30.1 67.45 1014.23 39.04 434.01
12.19 41.17 1019.43 65.47 475.64
13.11 41.58 1020.43 74.32 460.44
28.29 68.67 1005.46 69.22 436.4
13.45 40.73 1018.7 93.88 461.03
10.98 41.54 1019.94 69.83 479.08
26.48 69.14 1009.31 84.11 435.76
13.07 45.51 1015.22 78.65 460.14
25.56 75.6 1017.37 69.31 442.2
22.68 50.78 1008.83 70.3 447.69
28.86 73.67 1006.65 68.23 431.15
22.7 63.56 1014.32 71.76 445.0
27.89 73.21 1001.32 85.88 431.59
13.78 44.47 1027.94 71.09 467.22
28.14 51.43 1012.16 52.67 445.33
11.8 45.09 1013.21 89.68 470.57
10.71 39.61 1018.72 73.66 473.77
24.54 60.29 1017.42 58.94 447.67
11.54 40.05 1014.78 87.05 474.29
29.47 71.32 1008.07 67.0 437.14
29.24 69.05 1003.12 43.18 432.56
14.51 41.79 1009.72 80.62 459.14
22.91 60.07 1016.03 59.72 446.19
27.02 71.77 1006.38 72.1 428.1
13.49 44.47 1030.18 69.15 468.46
30.24 66.75 1017.95 55.66 435.02
23.19 48.6 1002.38 61.19 445.52
17.73 40.55 1003.36 74.62 462.69
18.62 61.27 1019.26 73.35 455.75
12.85 40.0 1015.89 68.85 463.74
32.33 69.68 1011.95 39.89 439.79
25.09 58.95 1016.99 53.16 443.26
29.45 69.13 1009.3 52.97 432.04
16.91 43.96 1013.32 79.87 465.86
14.09 45.87 1009.05 84.09 465.6
10.73 25.36 1009.35 100.15 469.43
23.2 49.3 1003.4 79.77 440.75
8.21 38.91 1015.82 88.99 481.32
9.3 40.56 1022.64 76.14 479.87
16.97 39.16 1005.7 69.13 458.59
23.69 71.97 1009.62 93.03 438.62
25.13 59.44 1012.38 77.92 445.59
9.86 43.56 1015.13 74.89 481.87
11.33 41.5 1013.58 88.7 475.01
26.95 48.41 1008.53 62.94 436.54
15.0 40.66 1016.28 89.62 456.63
20.76 62.52 1015.63 81.04 451.69
14.29 39.59 1010.93 94.53 463.04
19.74 67.71 1007.68 64.02 446.1
26.68 59.92 1009.94 70.57 438.67
14.24 41.4 1019.7 70.32 466.88
21.98 48.41 1008.42 84.86 444.6
22.75 59.39 1015.4 81.41 440.26
8.34 40.96 1023.28 89.45 483.92
11.8 41.2 1017.18 82.71 475.19
8.81 44.68 1023.06 93.93 479.24
30.05 73.68 1014.95 70.6 434.92
16.01 65.46 1014.0 87.68 454.16
21.75 58.79 1012.42 87.58 447.58
13.94 41.26 1021.67 74.4 467.9
29.25 69.13 1010.27 67.35 426.29
22.33 45.87 1007.8 63.61 447.02
16.43 41.79 1005.47 76.89 455.85
11.5 40.22 1010.31 78.08 476.46
23.53 68.94 1007.53 69.17 437.48
21.86 49.21 1014.61 53.31 452.77
6.17 39.33 1012.57 93.32 491.54
30.19 64.79 1017.22 42.47 438.41
11.67 41.93 1019.81 82.58 476.1
15.34 36.99 1007.87 94.59 464.58
11.5 40.78 1023.91 86.31 467.74
25.53 57.17 1010.0 72.57 442.12
21.27 57.5 1014.53 80.76 453.34
28.37 69.13 1010.44 71.93 425.29
28.39 51.43 1011.74 47.54 449.63
13.78 45.78 1025.27 95.72 462.88
14.6 42.32 1015.71 77.03 464.67
5.1 35.57 1027.17 80.49 489.96
7.0 38.08 1020.27 77.67 482.38
26.3 77.95 1009.45 78.72 437.95
30.56 71.98 1004.74 58.77 429.2
21.09 46.63 1013.03 74.8 453.34
28.21 70.02 1010.58 51.34 442.47
15.84 49.69 1015.14 90.41 462.6
10.03 40.96 1024.57 91.1 478.79
20.37 52.05 1012.34 62.57 456.11
21.19 50.16 1005.81 84.27 450.33
33.73 69.88 1007.21 42.93 434.83
29.87 73.68 1015.1 40.96 433.43
19.62 62.96 1020.76 76.53 456.02
9.93 40.67 1018.08 69.74 485.23
9.43 37.14 1013.03 74.99 473.57
14.24 39.58 1011.17 70.45 469.94
12.97 49.83 1008.69 91.49 452.07
7.6 41.04 1021.82 88.97 475.32
8.39 36.24 1013.39 89.13 480.69
25.41 48.06 1013.12 46.52 444.01
18.43 56.03 1020.41 60.55 465.17
10.31 39.82 1012.87 88.71 480.61
11.29 41.5 1013.39 89.15 476.04
22.61 49.3 1003.51 83.02 441.76
29.34 71.98 1005.19 75.19 428.24
18.87 67.71 1004.0 87.35 444.77
13.21 45.87 1008.58 85.66 463.1
11.3 44.6 1018.19 91.66 470.5
29.23 72.99 1007.04 63.47 431.0
27.76 69.4 1004.27 72.25 430.68
29.26 67.17 1006.6 70.58 436.42
25.72 49.82 1016.19 60.1 452.33
23.43 63.94 1010.64 89.29 440.16
25.6 63.76 1010.18 67.43 435.75
22.3 44.57 1008.48 67.58 449.74
27.91 72.24 1010.74 70.8 430.73
30.35 77.17 1009.55 63.62 432.75
21.78 47.43 1007.88 66.68 446.79
7.19 41.39 1018.12 90.76 486.35
20.88 59.8 1015.66 75.34 453.18
24.19 50.23 1015.73 59.77 458.31
9.98 41.54 1019.7 80.79 480.26
23.47 51.3 1011.89 74.1 448.65
26.35 49.5 1012.67 41.34 458.41
29.89 64.69 1006.37 58.78 435.39
19.29 50.16 1010.49 97.78 450.21
17.48 43.14 1018.68 74.85 459.59
25.21 75.6 1017.19 69.84 445.84
23.3 48.78 1018.17 75.36 441.08
15.42 37.85 1009.89 85.8 467.33
21.44 63.09 1016.56 90.11 444.19
29.45 68.27 1007.96 61.63 432.96
29.69 47.93 1002.85 44.76 438.09
15.52 36.99 1006.86 89.7 467.9
11.47 43.67 1012.68 72.51 475.72
9.77 34.69 1027.72 74.98 477.51
22.6 69.84 1006.37 79.59 435.13
8.24 39.61 1017.99 78.42 477.9
17.01 44.2 1019.18 61.23 457.26
19.64 44.6 1015.88 47.56 467.53
10.61 41.58 1021.08 93.06 465.15
12.04 40.1 1014.42 89.65 474.28
29.19 65.71 1013.85 50.5 444.49
21.75 45.09 1014.15 44.84 452.84
23.66 77.54 1008.5 85.32 435.38
27.05 75.33 1003.88 82.94 433.57
29.63 69.71 1009.04 67.26 435.27
18.2 39.63 1005.35 79.05 468.49
32.22 70.8 1009.9 62.03 433.07
26.88 73.56 1004.85 94.36 430.63
29.05 65.74 1013.29 60.02 440.74
8.9 39.96 1026.31 95.46 474.49
18.93 48.6 1005.72 84.92 449.74
27.49 63.76 1010.09 62.8 436.73
23.1 70.79 1006.53 90.81 434.58
11.22 43.13 1017.24 80.9 473.93
31.97 79.74 1007.03 55.84 435.99
13.32 43.22 1009.45 75.3 466.83
31.68 68.24 1005.29 37.34 427.22
23.69 63.77 1013.39 79.5 444.07
13.83 41.49 1020.11 87.29 469.57
18.32 66.51 1015.18 81.5 459.89
11.05 40.71 1024.91 76.42 479.59
22.03 64.69 1007.21 75.75 440.92
10.23 41.46 1020.45 84.95 480.87
23.92 66.54 1009.93 62.37 441.9
29.38 69.68 1011.35 49.25 430.2
17.35 42.86 1014.62 74.16 465.16
9.81 44.45 1021.19 90.55 471.32
4.97 40.64 1020.91 94.28 485.43
5.15 40.07 1012.27 63.31 495.35
21.54 58.49 1010.85 78.9 449.12
7.94 42.02 1006.22 90.97 480.53
18.77 50.66 1014.89 87.34 457.07
21.69 69.94 1010.7 80.8 443.67
10.07 44.68 1023.44 90.95 477.52
13.83 39.64 1012.52 69.97 472.95
10.45 39.69 1003.92 89.45 472.54
11.56 40.71 1015.85 76.08 469.17
23.64 70.04 1011.09 83.35 435.21
10.48 40.22 1004.81 92.16 477.78
13.09 39.85 1012.86 58.42 475.89
10.67 40.23 1017.75 85.06 483.9
12.57 39.16 1016.53 88.91 476.2
14.45 43.34 1015.47 83.33 462.16
14.22 37.85 1011.24 88.49 471.05
6.97 41.26 1010.6 96.88 484.71
20.61 63.86 1015.43 73.86 446.34
14.67 42.28 1007.21 65.17 469.02
29.06 72.86 1004.23 69.41 432.12
14.38 40.1 1015.51 81.23 467.28
32.51 69.98 1013.29 54.07 429.66
11.79 45.09 1013.16 89.17 469.49
8.65 40.56 1023.23 78.85 485.87
9.75 40.81 1026.0 84.44 481.95
9.11 40.02 1031.1 83.02 479.03
23.39 69.13 1010.99 90.66 434.5
14.3 54.3 1015.16 75.29 464.9
17.49 63.94 1020.02 82.6 452.71
31.1 69.51 1010.84 45.4 429.74
19.77 56.65 1020.67 66.33 457.09
28.61 72.29 1011.61 45.33 446.77
13.52 41.48 1014.46 67.12 460.76
13.52 40.83 1008.31 84.14 471.95
17.57 46.21 1014.09 80.81 453.29
28.18 60.07 1016.34 49.13 441.61
14.29 46.18 1017.01 87.29 464.73
18.12 43.69 1016.91 52.95 464.68
31.27 73.91 1003.72 68.92 430.59
26.24 77.95 1014.19 85.21 438.01
7.44 41.04 1021.84 88.56 479.08
29.78 74.78 1009.28 55.09 436.39
23.37 65.46 1016.25 48.64 447.07
10.62 39.58 1011.9 87.85 479.91
5.84 43.02 1013.88 87.42 489.05
14.51 53.82 1016.46 62.75 463.17
11.31 42.02 1001.18 94.86 471.26
11.25 40.67 1011.64 63.54 480.49
9.18 39.42 1025.41 69.46 473.78
19.82 58.16 1016.76 74.66 455.5
24.77 58.41 1013.78 80.57 446.27
9.66 41.06 1021.21 84.7 482.2
21.96 59.8 1016.72 72.6 452.48
18.59 43.14 1011.92 52.63 464.48
24.75 69.89 1015.29 82.01 438.1
24.37 63.47 1012.77 75.22 445.6
29.6 67.79 1010.37 51.05 442.43
25.32 61.25 1011.56 80.1 436.67
16.15 41.85 1016.54 81.58 466.56
15.74 71.14 1019.65 65.94 457.29
5.97 36.25 1029.65 86.74 487.03
15.84 52.72 1026.45 62.57 464.93
14.84 44.63 1019.28 57.37 466.0
12.25 48.79 1017.44 88.91 469.52
27.38 70.04 1011.18 72.26 428.88
8.76 41.48 1018.49 74.98 474.3
15.54 39.31 1009.69 71.19 461.06
18.71 39.39 1014.09 62.82 465.57
13.06 41.78 1012.3 55.31 467.67
12.72 40.71 1016.02 71.57 466.99
19.83 39.39 1013.73 59.16 463.72
27.23 49.16 1004.03 40.8 443.78
24.27 68.28 1005.43 67.63 445.23
11.8 40.66 1017.13 97.2 464.43
6.76 36.25 1028.31 91.16 484.36
25.99 63.07 1012.5 64.81 442.16
16.3 39.63 1004.64 85.61 464.11
16.5 49.39 1018.35 93.42 462.48
10.59 42.49 1009.59 77.36 477.49
26.05 65.59 1012.78 67.03 437.04
19.5 40.79 1003.8 89.45 457.09
22.21 45.01 1012.22 54.84 450.6
17.86 45.0 1023.25 53.48 465.78
29.96 70.04 1010.15 54.47 427.1
19.08 44.63 1020.14 43.36 459.81
23.59 47.43 1006.64 48.92 447.36
3.38 39.64 1011.0 81.22 488.92
26.39 66.49 1012.96 60.35 433.36
8.99 39.04 1021.99 75.98 483.35
10.91 41.04 1026.57 74.24 469.53
13.08 39.82 1012.27 85.21 476.96
23.95 58.46 1017.5 68.46 440.75
15.64 43.71 1024.51 78.31 462.55
18.78 54.2 1012.05 89.25 448.04
20.65 50.59 1016.22 68.57 455.24
4.96 40.07 1011.8 67.38 494.75
23.51 57.32 1012.55 53.6 444.58
5.99 35.79 1011.56 91.69 484.82
23.65 66.05 1019.6 78.21 442.9
5.17 39.33 1009.68 94.19 485.46
26.38 49.5 1012.82 37.19 457.81
6.02 43.65 1013.85 83.53 481.92
23.2 61.02 1009.63 79.45 443.23
8.57 39.69 1000.91 99.9 474.29
30.72 71.58 1009.98 50.39 430.46
21.52 50.66 1013.56 74.33 455.71
22.93 62.26 1011.25 83.66 438.34
5.71 41.31 1003.24 89.48 485.83
18.62 44.06 1017.76 64.59 452.82
27.88 68.94 1007.68 75.68 435.04
22.32 59.8 1016.82 64.18 451.21
14.55 42.74 1028.41 70.09 465.81
17.83 44.92 1025.04 70.58 458.42
9.68 39.96 1026.09 99.28 470.22
19.41 49.39 1020.84 81.89 449.24
13.22 44.92 1023.84 87.99 471.43
12.24 44.92 1023.74 88.21 473.26
19.21 58.49 1011.7 91.29 452.82
29.74 70.32 1008.1 52.72 432.69
23.28 60.84 1017.91 67.5 444.13
8.02 41.92 1029.8 92.05 467.21
22.47 48.6 1002.33 63.23 445.98
27.51 73.77 1002.42 90.88 436.91
17.51 44.9 1009.05 74.91 455.01
23.22 66.56 1002.47 85.39 437.11
11.73 40.64 1020.68 96.98 477.06
21.19 67.71 1006.65 56.28 441.71
5.48 40.07 1019.63 65.62 495.76
24.26 66.44 1011.33 55.32 445.63
12.32 41.62 1012.88 88.88 464.72
31.26 68.94 1005.94 39.49 438.03
32.09 72.86 1003.47 54.59 434.78
24.98 60.32 1015.63 57.19 444.67
27.48 61.41 1012.2 45.06 452.24
21.04 45.09 1014.19 40.62 450.92
27.75 70.4 1006.65 90.21 436.53
22.79 71.77 1005.75 90.91 435.53
24.22 68.51 1013.23 74.96 440.01
27.06 64.45 1008.72 54.21 443.1
29.25 71.94 1007.18 63.62 427.49
26.86 68.08 1012.99 50.04 436.25
29.64 67.79 1009.99 51.23 440.74
19.92 63.31 1015.02 82.71 443.54
18.5 51.43 1010.82 92.04 459.42
23.71 60.23 1009.76 90.67 439.66
14.39 44.84 1023.55 91.14 464.15
19.3 56.65 1020.55 70.43 459.1
24.65 52.36 1014.76 66.63 455.68
13.5 45.51 1015.33 86.95 469.08
9.82 41.26 1007.71 96.69 478.02
18.4 44.06 1017.36 70.88 456.8
28.12 44.89 1009.18 47.14 441.13
17.15 43.69 1017.05 63.36 463.88
30.69 73.67 1006.14 60.58 430.45
28.82 65.71 1014.24 54.3 449.18
21.3 48.92 1010.92 65.09 447.89
30.58 70.04 1010.4 48.16 431.59
21.17 52.3 1009.36 81.51 447.5
9.87 41.82 1033.04 68.57 475.58
22.18 59.8 1016.77 73.16 453.24
24.39 63.21 1012.59 80.88 446.4
10.73 44.92 1025.1 85.4 476.81
9.38 40.46 1019.29 75.77 474.1
20.27 57.76 1016.66 75.76 450.71
24.82 66.48 1006.4 70.21 433.62
16.55 41.66 1011.45 55.53 465.14
20.73 59.87 1019.08 80.48 445.18
9.51 39.22 1015.3 72.41 474.12
8.63 43.79 1016.08 83.25 483.91
6.48 40.27 1010.55 82.12 486.68
14.95 43.52 1022.43 94.75 464.98
5.76 45.87 1010.83 95.79 481.4
10.94 39.04 1021.81 86.02 479.2
15.87 41.16 1005.85 78.29 463.86
12.42 38.25 1012.76 82.23 472.3
29.12 58.84 1001.31 52.86 446.51
29.12 51.43 1005.93 60.66 437.71
19.08 41.1 1001.96 62.77 458.94
31.06 67.17 1007.62 65.54 437.91
5.72 39.33 1009.96 95.4 490.76
26.52 65.06 1013.4 51.78 439.66
13.84 44.9 1007.58 63.62 463.27
13.03 39.52 1016.68 83.09 473.99
25.94 66.49 1012.83 61.81 433.38
16.64 53.82 1015.13 68.24 459.01
14.13 40.75 1016.05 72.41 471.44
13.65 39.28 1012.97 79.64 471.91
14.5 44.47 1028.2 66.95 465.15
19.8 51.19 1008.25 91.98 446.66
25.2 63.76 1009.78 64.96 438.15
20.66 51.19 1008.81 88.93 447.14
12.07 43.71 1025.53 85.62 472.32
25.64 70.72 1010.16 84.0 441.68
23.33 72.99 1009.33 89.41 440.04
29.41 64.05 1009.82 67.4 444.82
16.6 53.16 1014.5 76.75 457.26
27.53 72.58 1009.13 89.06 428.83
20.62 43.43 1009.93 64.02 449.07
26.02 71.94 1009.38 64.12 435.21
12.75 44.2 1017.59 81.22 471.03
12.87 48.04 1012.47 100.13 465.56
25.77 62.96 1019.86 58.07 442.83
14.84 41.48 1017.26 63.42 460.3
7.41 40.71 1023.07 83.32 474.25
8.87 41.82 1033.3 74.28 477.97
9.69 40.46 1019.1 71.91 472.16
16.17 46.97 1014.22 85.8 456.08
26.24 49.82 1014.9 55.58 452.41
13.78 43.22 1011.31 69.7 463.71
26.3 67.07 1006.26 63.79 433.72
17.37 57.76 1016.0 86.59 456.4
23.6 48.98 1015.41 48.28 448.43
8.3 36.08 1020.63 80.42 481.6
18.86 42.18 1001.16 98.58 457.07
22.12 49.39 1019.8 72.83 451.0
28.41 75.6 1018.48 56.07 440.28
29.42 71.32 1002.26 67.13 437.47
18.61 67.71 1004.07 84.49 443.57
27.57 69.84 1004.91 68.37 426.6
12.83 41.5 1013.12 86.07 470.87
9.64 39.85 1012.9 83.82 478.37
19.13 58.66 1013.32 74.86 453.92
15.92 40.56 1020.79 53.52 470.22
24.64 72.24 1011.37 80.61 434.54
27.62 63.9 1013.11 43.56 442.89
8.9 36.24 1013.29 89.35 479.03
9.55 43.99 1020.5 97.28 476.06
10.57 36.71 1022.62 80.49 473.88
19.8 57.25 1010.84 88.9 451.75
25.63 56.85 1012.68 49.7 439.2
24.7 58.46 1015.58 68.64 439.7
15.26 46.18 1013.68 98.58 463.6
20.06 52.84 1004.21 82.12 447.47
19.84 56.89 1013.23 78.32 447.92
11.49 44.63 1020.44 86.04 471.08
23.74 72.43 1007.99 91.36 437.55
22.62 51.3 1012.36 81.02 448.27
29.53 72.39 998.47 76.05 431.69
21.32 48.14 1016.57 71.81 449.09
20.3 58.46 1015.93 82.13 448.79
16.97 44.92 1025.21 74.27 460.21
12.07 41.17 1013.54 71.32 479.28
7.46 41.82 1032.67 74.59 483.11
19.2 54.2 1011.46 84.44 450.75
28.64 66.54 1010.43 43.39 437.97
13.56 41.48 1008.53 87.2 459.76
17.4 44.9 1020.5 77.11 457.75
14.08 40.1 1015.48 82.81 469.33
27.11 69.75 1009.74 85.67 433.28
20.92 70.02 1010.23 95.58 444.64
16.18 44.9 1021.3 74.46 463.1
15.57 44.68 1022.01 90.02 460.91
10.37 39.04 1023.95 81.93 479.35
19.6 59.21 1017.65 86.29 449.23
9.22 40.92 1021.83 85.43 474.51
27.76 72.99 1007.81 71.66 435.02
28.68 70.72 1009.43 71.33 435.45
20.95 48.14 1013.3 67.72 452.38
9.06 39.3 1019.73 84.23 480.41
9.21 39.72 1019.54 74.44 478.96
13.65 42.74 1026.58 71.48 468.87
31.79 76.2 1007.89 56.3 434.01
14.32 44.6 1013.85 68.13 466.36
26.28 75.23 1011.44 68.35 435.28
7.69 43.02 1014.51 85.23 486.46
14.44 40.1 1015.51 79.78 468.19
9.19 41.01 1022.14 98.98 468.37
13.35 41.39 1019.17 72.87 474.19
23.04 74.22 1009.52 90.93 440.32
4.83 38.44 1015.35 72.94 485.32
17.29 42.86 1014.38 72.3 464.27
8.73 36.18 1013.66 77.74 479.25
26.21 70.32 1007.0 78.29 430.4
23.72 58.62 1016.65 69.1 447.49
29.27 64.69 1006.85 55.79 438.23
10.4 40.43 1025.46 75.09 492.09
12.19 40.75 1015.13 88.98 475.36
20.4 54.9 1016.68 64.26 452.56
34.3 74.67 1015.98 25.89 427.84
27.56 68.08 1010.8 59.18 433.95
30.9 70.8 1008.48 67.48 435.27
14.85 58.59 1014.04 89.85 454.62
16.42 40.56 1020.36 50.62 472.17
16.45 63.31 1015.96 83.97 452.42
10.14 42.02 1003.19 96.51 472.17
9.53 41.44 1018.01 80.09 481.83
17.01 49.15 1021.83 84.02 458.78
23.94 62.08 1022.47 61.97 447.5
15.95 49.25 1019.04 88.51 463.4
11.15 41.26 1022.67 81.83 473.57
25.56 70.32 1009.07 90.63 433.72
27.16 66.44 1011.2 73.37 431.85
26.71 77.95 1012.13 77.5 433.47
29.56 74.22 1007.45 57.46 432.84
31.19 70.94 1007.29 51.91 436.6
6.86 41.17 1020.12 79.14 490.23
12.36 41.74 1020.58 69.24 477.16
32.82 68.31 1010.44 41.85 441.06
25.3 70.98 1007.22 95.1 440.86
8.71 41.82 1033.08 74.53 477.94
13.34 40.8 1026.56 64.85 474.47
14.2 43.02 1012.18 57.07 470.67
23.74 65.34 1013.7 62.9 447.31
16.9 44.88 1018.14 72.21 466.8
28.54 71.94 1007.4 65.99 430.91
30.15 69.88 1007.2 73.67 434.75
14.33 42.86 1010.82 88.59 469.52
25.57 59.43 1008.88 61.19 438.9
30.55 70.04 1010.51 49.37 429.56
28.04 74.33 1013.53 48.65 432.92
26.39 49.16 1005.68 56.18 442.87
15.3 41.76 1022.57 71.56 466.59
6.03 41.14 1028.04 87.46 479.61
13.49 44.63 1019.12 70.02 471.08
27.67 59.14 1016.51 61.2 433.37
24.19 65.48 1018.8 60.54 443.92
24.44 59.14 1016.74 71.82 443.5
29.86 64.79 1017.37 44.8 439.89
30.2 69.59 1008.9 67.32 434.66
7.99 41.38 1021.95 78.77 487.57
9.93 41.62 1013.76 96.02 464.64
11.03 42.32 1017.26 90.56 470.92
22.34 63.73 1014.37 83.19 444.39
25.33 48.6 1002.54 68.45 442.48
18.87 52.08 1005.25 99.19 449.61
25.97 69.34 1009.43 88.11 435.02
16.58 43.99 1021.81 79.29 458.67
14.35 46.18 1016.63 87.76 461.74
25.06 62.39 1008.09 82.56 438.31
13.85 48.92 1011.68 79.24 462.38
16.09 44.2 1019.39 67.24 460.56
26.34 59.21 1013.37 58.98 439.22
23.01 58.79 1009.71 84.22 444.64
26.39 71.25 999.8 89.12 430.34
31.32 71.29 1008.37 50.07 430.46
16.64 45.87 1009.02 98.86 456.79
13.42 41.23 994.17 95.79 468.82
20.06 44.9 1008.79 70.06 448.51
14.8 44.71 1014.67 41.71 470.77
12.59 41.14 1025.79 86.55 465.74
26.7 66.56 1005.31 71.97 430.21
19.78 50.32 1008.62 96.4 449.23
15.17 49.15 1021.91 91.73 461.89
21.71 61.45 1010.97 91.62 445.72
19.09 39.39 1013.36 59.14 466.13
19.76 51.19 1008.38 92.56 448.71
14.68 41.23 998.43 83.71 469.25
21.3 66.86 1013.04 55.43 450.56
16.73 39.64 1008.94 74.91 464.46
12.26 41.5 1014.87 89.41 471.13
14.77 48.06 1010.92 69.81 461.52
18.26 59.15 1012.04 86.01 451.09
27.1 79.74 1005.43 86.05 431.51
14.72 40.83 1009.65 80.98 469.8
26.3 51.43 1012.05 63.62 442.28
16.48 48.92 1011.84 64.16 458.67
17.99 43.79 1016.13 75.63 462.4
20.34 59.8 1015.18 80.21 453.54
25.53 62.96 1019.81 59.7 444.38
31.59 58.9 1003.39 47.6 440.52
30.8 69.14 1007.68 63.78 433.62
10.75 45.0 1023.68 89.37 481.96
19.3 44.9 1008.89 70.55 452.75
4.71 39.42 1026.4 84.42 481.28
23.1 66.05 1020.28 80.62 439.03
32.63 73.88 1005.64 52.56 435.75
26.63 74.16 1009.72 83.26 436.03
24.35 58.49 1011.03 70.64 445.6
15.11 56.03 1020.27 89.95 462.65
29.1 50.05 1005.87 51.53 438.66
21.24 50.32 1008.54 84.83 447.32
6.16 39.48 1004.85 59.68 484.55
7.36 41.01 1024.9 97.88 476.8
10.44 39.04 1023.99 85.03 480.34
26.76 48.41 1010.53 47.38 440.63
16.79 44.6 1014.27 48.08 459.48
10.76 40.43 1025.98 79.65 490.78
6.07 38.91 1019.25 83.39 483.56
27.33 73.18 1012.26 82.18 429.38
27.15 59.21 1013.49 51.71 440.27
22.35 51.43 1011.34 77.33 445.34
21.82 65.27 1013.86 72.81 447.43
21.11 69.94 1004.37 84.26 439.91
19.95 50.59 1016.11 73.23 459.27
7.45 39.61 1017.88 79.73 478.89
15.36 41.66 1012.41 62.32 466.7
15.65 43.5 1021.39 78.58 463.5
25.31 74.33 1015.04 79.88 436.21
25.88 63.47 1011.95 65.87 443.94
24.6 63.94 1012.87 80.28 439.63
22.58 41.54 1013.21 71.33 460.95
19.69 59.14 1015.99 70.33 448.69
25.85 75.08 1006.24 57.73 444.63
10.06 37.83 1005.49 99.46 473.51
18.59 39.54 1008.56 68.61 462.56
18.27 50.16 1011.07 95.91 451.76
8.85 40.43 1025.68 80.42 491.81
30.04 68.08 1011.04 51.01 429.52
26.06 49.02 1007.59 74.08 437.9
14.8 38.73 1003.18 80.73 467.54
23.93 64.45 1015.35 54.71 449.97
23.72 66.48 1003.61 73.75 436.62
11.44 40.55 1023.37 88.43 477.68
20.28 63.86 1016.04 74.66 447.26
27.9 63.13 1011.8 70.04 439.76
24.74 59.39 1015.23 74.64 437.49
14.8 58.2 1018.29 85.11 455.14
8.22 41.03 1021.76 82.97 485.5
27.56 66.93 1016.81 55.59 444.1
32.07 70.94 1006.91 49.9 432.33
9.53 44.03 1008.87 89.99 471.23
13.61 42.34 1017.93 91.61 463.89
22.2 51.19 1009.2 82.95 445.54
21.36 59.54 1007.99 92.62 446.09
23.25 63.86 1017.82 59.64 445.12
23.5 59.21 1018.29 63.0 443.31
8.46 39.66 1015.14 85.38 484.16
8.19 40.69 1019.86 85.23 477.76
30.67 71.29 1008.36 52.08 430.28
32.48 62.04 1010.39 38.05 446.48
8.99 36.66 1028.11 71.98 481.03
13.77 47.83 1007.41 90.66 466.07
19.05 67.32 1013.2 83.14 447.47
21.19 55.5 1019.83 65.22 455.93
10.12 40.0 1021.15 91.67 479.62
24.93 47.01 1014.28 66.04 455.06
8.47 40.46 1019.87 78.19 475.06
24.52 56.85 1012.59 54.47 438.89
28.55 69.84 1003.38 67.26 432.7
20.58 50.9 1011.89 72.56 452.6
18.31 46.21 1010.46 82.15 451.75
27.18 71.06 1008.16 86.32 430.66
4.43 38.91 1019.04 88.17 491.9
26.02 74.78 1010.04 72.78 439.82
15.75 39.0 1015.91 69.58 460.73
22.99 60.95 1015.14 69.86 449.7
25.52 59.15 1013.88 65.37 439.42
27.04 65.06 1013.33 52.37 439.84
6.42 35.57 1025.58 79.63 485.86
17.04 40.12 1011.81 83.14 458.1
10.79 39.82 1012.89 88.25 479.92
20.41 56.03 1019.94 55.85 458.29
7.36 40.07 1017.29 52.55 489.45
28.08 73.42 1012.17 62.74 434.0
24.74 69.13 1010.69 90.08 431.24
28.32 47.93 1003.26 54.5 439.5
16.71 40.56 1019.48 49.88 467.46
30.7 71.58 1010.0 48.96 429.27
18.42 58.95 1016.95 86.77 452.1
10.62 42.02 999.83 96.66 472.41
22.18 69.05 1002.75 70.84 442.14
22.38 49.3 1003.56 83.83 441.0
13.94 41.58 1020.76 68.22 463.07
21.24 60.84 1017.99 82.22 445.71
6.76 39.81 1017.11 87.9 483.16
26.73 68.84 1010.75 66.83 440.45
7.24 38.06 1020.6 85.36 481.83
10.84 40.62 1015.53 60.9 467.6
19.32 52.84 1004.29 83.51 450.88
29.0 69.13 1001.22 52.96 425.5
23.38 54.42 1013.95 73.02 451.87
31.17 69.51 1010.51 43.11 428.94
26.17 48.6 1002.59 61.41 439.86
30.9 73.42 1011.21 65.32 433.44
24.92 73.68 1015.12 93.68 438.23
32.77 71.32 1007.68 42.39 436.95
14.37 40.56 1021.67 68.18 470.19
8.36 40.22 1011.6 89.18 484.66
31.45 68.27 1007.56 64.79 430.81
31.6 73.17 1010.05 43.48 433.37
17.9 48.98 1014.17 80.4 453.02
20.35 50.9 1012.6 72.43 453.5
16.21 41.23 995.88 80.0 463.09
19.36 44.6 1016.25 45.65 464.56
21.04 65.46 1017.22 63.02 452.12
14.05 40.69 1015.66 74.39 470.9
23.48 64.15 1021.08 57.77 450.89
21.91 63.76 1009.85 76.8 445.04
24.42 63.07 1011.49 67.39 444.72
14.26 40.92 1022.07 73.96 460.38
21.38 58.33 1013.05 72.75 446.8
15.71 44.06 1018.34 71.69 465.05
5.78 40.62 1016.55 84.98 484.13
6.77 39.81 1017.01 87.68 488.27
23.84 49.21 1013.85 50.36 447.09
21.17 58.16 1017.16 68.11 452.02
19.94 58.96 1014.16 66.27 455.55
8.73 41.92 1029.41 89.72 480.99
16.39 41.67 1012.96 61.07 467.68

Linear Regression Model

  • Linear Regression is one of the most useful work-horses of statistical learning
  • See Chapter 7 of Kevin Murphy's Machine Learning froma Probabilistic Perspective for a good mathematical and algorithmic introduction.
  • You should have already seen Ameet's treatment of the topic from earlier notebook.
// First let's hold out 20% of our data for testing and leave 80% for training
var Array(split20, split80) = dataset.randomSplit(Array(0.20, 0.80), 1800009193L)
split20: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
split80: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
// Let's cache these datasets for performance
val testSet = split20.cache()
val trainingSet = split80.cache()
testSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
trainingSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
testSet.count() // action to actually cache
res87: Long = 1856
trainingSet.count() // action to actually cache
res88: Long = 7712

Let's take a few elements of the three DataFrames.

dataset.take(3)
res89: Array[org.apache.spark.sql.Row] = Array([14.96,41.76,1024.07,73.17,463.26], [25.18,62.96,1020.04,59.08,444.37], [5.11,39.4,1012.16,92.14,488.56])
testSet.take(3)
res90: Array[org.apache.spark.sql.Row] = Array([2.34,39.42,1028.47,69.68,490.34], [2.8,39.64,1011.01,82.96,482.66], [3.82,35.47,1016.62,84.34,489.04])
trainingSet.take(3)
res91: Array[org.apache.spark.sql.Row] = Array([1.81,39.42,1026.92,76.97,490.55], [2.58,39.42,1028.68,69.03,488.69], [2.64,39.64,1011.02,85.24,481.29])
// ***** LINEAR REGRESSION MODEL ****

import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline

// Let's initialize our linear regression learner
val lr = new LinearRegression()
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
lr: org.apache.spark.ml.regression.LinearRegression = linReg_3b8dffe4015f
// We use explain params to dump the parameters we can use
lr.explainParams()
res92: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)

The cell below is based on the Spark ML pipeline API. More information can be found in the Spark ML Programming Guide at https://spark.apache.org/docs/latest/ml-guide.html

// Now we set the parameters for the method
lr.setPredictionCol("Predicted_PE")
  .setLabelCol("PE")
  .setMaxIter(100)
  .setRegParam(0.1)
// We will use the new spark.ml pipeline API. If you have worked with scikit-learn this will be very familiar.
val lrPipeline = new Pipeline()
lrPipeline.setStages(Array(vectorizer, lr))
// Let's first train on the entire dataset to see what we get
val lrModel = lrPipeline.fit(trainingSet)
lrPipeline: org.apache.spark.ml.Pipeline = pipeline_07136143d91d
lrModel: org.apache.spark.ml.PipelineModel = pipeline_07136143d91d

Since Linear Regression is simply a line of best fit over the data that minimizes the square of the error, given multiple input dimensions we can express each predictor as a line function of the form:

\[ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_3 + \ldots + b_i x_i + \ldots + b_k x_k \]

where \(b_0\) is the intercept and \(b_i\)'s are coefficients.

To express the coefficients of that line we can retrieve the Estimator stage from the fitted, linear-regression pipeline model named lrModel and express the weights and the intercept for the function.

// The intercept is as follows:
val intercept = lrModel.stages(1).asInstanceOf[LinearRegressionModel].intercept
intercept: Double = 434.0183482461227
// The coefficents (i.e. weights) are as follows:

val weights = lrModel.stages(1).asInstanceOf[LinearRegressionModel].coefficients.toArray
weights: Array[Double] = Array(-1.9288465830313846, -0.24931659465920197, 0.08140785421485836, -0.1458797521995801)

The model has been fit and the intercept and coefficients displayed above.

Now, let us do some work to make a string of the model that is easy to understand for an applied data scientist or data analyst.

val featuresNoLabel = dataset.columns.filter(col => col != "PE")
featuresNoLabel: Array[String] = Array(AT, V, AP, RH)
val coefficentFeaturePairs = sc.parallelize(weights).zip(sc.parallelize(featuresNoLabel))
coefficentFeaturePairs: org.apache.spark.rdd.RDD[(Double, String)] = ZippedPartitionsRDD2[32671] at zip at command-2972105651606305:1
coefficentFeaturePairs.collect() // this just pairs each coefficient with the name of its corresponding feature
res93: Array[(Double, String)] = Array((-1.9288465830313846,AT), (-0.24931659465920197,V), (0.08140785421485836,AP), (-0.1458797521995801,RH))
// Now let's sort the coefficients from the largest to the smallest

var equation = s"y = $intercept "
//var variables = Array
coefficentFeaturePairs.sortByKey().collect().foreach({
  case (weight, feature) =>
  { 
        val symbol = if (weight > 0) "+" else "-"
        val absWeight = Math.abs(weight)
        equation += (s" $symbol (${absWeight} * ${feature})")
  }
}
)
equation: String = y = 434.0183482461227  - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
// Finally here is our equation
println("Linear Regression Equation: " + equation)
Linear Regression Equation: y = 434.0183482461227  - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)

Based on examining the fitted Linear Regression Equation above:

  • There is a strong negative correlation between Atmospheric Temperature (AT) and Power Output due to the coefficient being greater than -1.91.
  • But our other dimenensions seem to have little to no correlation with Power Output.

Do you remember Step 2: Explore Your Data? When we visualized each predictor against Power Output using a Scatter Plot, only the temperature variable seemed to have a linear correlation with Power Output so our final equation seems logical.

Now let's see what our predictions look like given this model.

val predictionsAndLabels = lrModel.transform(testSet)

display(predictionsAndLabels.select("AT", "V", "AP", "RH", "PE", "Predicted_PE"))
AT V AP RH PE Predicted_PE
2.34 39.42 1028.47 69.68 490.34 493.23742177145215
2.8 39.64 1011.01 82.96 482.66 488.93663844863084
3.82 35.47 1016.62 84.34 489.04 488.2642491377776
3.98 35.47 1017.22 86.53 489.64 487.68500173970443
4.23 38.44 1016.46 76.64 489.0 487.8432005878593
4.32 35.47 1017.8 88.51 488.03 486.7875685475632
4.43 38.91 1019.04 88.17 491.9 485.8682911927764
4.65 35.19 1018.23 94.78 489.36 485.34119715268844
4.78 42.85 1013.39 93.36 481.47 482.9938172155284
4.87 42.85 1012.69 94.72 482.05 482.5648390621137
4.96 40.07 1011.8 67.38 494.75 487.00024243767876
4.97 42.85 1014.02 88.78 482.98 483.3467525779819
5.01 39.4 1003.69 91.9 475.34 482.8336530053327
5.06 40.64 1021.49 93.7 483.73 483.6145343498689
5.15 35.19 1018.63 93.94 488.42 484.53187599470635
5.15 40.07 1012.27 63.31 495.35 487.2657538698361
5.17 35.57 1026.68 79.86 491.32 487.10787889447494
5.21 41.31 1003.51 91.23 486.46 482.0547750131424
5.23 40.78 1025.13 92.74 483.12 483.688095258955
5.25 40.07 1019.48 67.7 495.23 487.0194077282659
5.28 45.87 1008.25 97.88 479.91 480.1986449575354
5.29 41.38 1020.62 83.83 492.12 484.35541367676683
5.42 41.38 1020.77 86.02 491.38 483.7973981417879
5.47 40.62 1018.66 83.61 481.56 484.07023605498495
5.51 41.03 1022.28 84.5 491.84 484.05572584065357
5.53 35.79 1011.19 94.01 484.64 483.0334383183464
5.54 41.38 1020.47 82.91 490.07 483.9952002249004
5.54 45.87 1008.69 95.91 478.02 480.02034741363497
5.56 45.87 1006.99 96.48 476.61 479.7602256710553
5.65 40.72 1022.46 85.17 487.09 483.7798894431585
5.7 40.62 1016.07 84.94 482.82 483.2217349280458
5.8 45.87 1009.14 92.06 481.6 480.1171178824119
5.82 40.78 1024.82 96.01 470.02 482.0478125504672
5.84 43.02 1013.88 87.42 489.05 481.81327159305386
5.97 36.25 1029.65 86.74 487.03 484.6333949755666
6.02 41.38 1021.2 88.71 490.57 482.2826790358646
6.03 41.17 1019.81 84.2 488.57 482.86050781997415
6.04 41.14 1027.8 86.4 480.39 483.17821215232124
6.04 41.14 1027.92 87.12 481.37 483.0829476732433
6.06 41.17 1019.67 84.7 489.62 482.71830544679335
6.13 40.64 1020.69 94.57 481.13 481.35862683823984
6.14 39.4 1011.21 90.87 485.94 481.4164995749685
6.15 40.77 1022.42 88.57 482.49 482.3037528502627
6.17 36.25 1028.68 90.59 483.77 483.6070229944035
6.22 39.33 1012.31 93.23 491.77 481.0249164343975
6.25 39.64 1010.98 83.45 483.43 482.2081944229683
6.3 41.14 1027.45 86.11 481.49 482.6905244198958
6.34 40.64 1020.62 94.39 478.78 480.9741288614041
6.38 40.07 1018.53 60.2 492.96 485.8565717694332
6.45 40.02 1032.08 79.7 481.36 483.99243959507345
6.48 40.27 1010.55 82.12 486.68 481.76650494734884
6.49 43.65 1020.41 72.78 484.94 483.069724719673
6.52 39.85 1012.55 86.36 483.01 481.33834961288795
6.59 39.37 1020.34 77.92 488.17 483.1883946104104
6.65 39.33 1011.29 92.85 490.41 480.1679106982307
6.71 40.72 1022.78 80.69 483.11 482.4149038683481
6.72 38.91 1016.89 90.47 485.89 480.94068220101354
6.76 39.22 1013.91 77.0 487.08 482.508645049916
6.8 41.16 1023.17 95.4 477.8 480.01746628251317
6.84 41.06 1021.04 89.59 489.96 480.63940670945976
6.86 38.08 1019.62 77.37 486.92 483.0108446487773
6.91 36.08 1021.82 84.31 486.37 482.57972730795177
6.91 39.37 1019.58 71.02 488.2 483.51586402481416
6.93 41.14 1027.18 84.67 479.06 481.6634377951154
6.99 35.19 1019.32 68.95 480.05 484.68450470880435
6.99 39.37 1020.19 75.06 487.18 482.8218608903564
7.05 43.65 1018.41 72.36 480.47 481.8880244206695
7.1 35.77 1015.39 92.1 480.36 480.6306788292839
7.11 41.74 1022.35 90.68 487.85 479.89671820679695
7.11 43.13 1018.96 87.82 486.11 479.6914116057231
7.24 38.06 1020.6 85.36 481.83 481.1970697561745
7.3 43.65 1019.33 67.62 482.96 482.17217802621536
7.34 40.72 1023.01 80.08 483.92 481.3074409763495
7.4 41.04 1024.44 90.9 477.69 479.64992318380445
7.41 40.71 1023.07 83.32 474.25 480.70714895561014
7.44 41.04 1021.84 88.56 479.08 479.7024675196716
7.45 39.61 1017.88 79.73 478.89 481.00544489343537
7.46 41.82 1032.67 74.59 483.11 482.38901084355183
7.48 38.5 1014.01 77.35 488.43 481.25646633043965
7.52 41.66 1015.2 78.41 483.28 480.33371483717946
7.54 38.56 1016.49 69.1 486.76 482.5311759738776
7.55 41.04 1027.03 83.32 476.58 480.67721106043905
7.55 43.65 1019.09 61.71 481.61 482.53257783094546
7.57 41.14 1028.23 87.97 477.8 480.0330510466423
7.6 41.04 1021.82 88.97 475.32 479.3324132109004
7.65 41.01 1024.31 97.17 475.89 478.2499419685471
7.67 41.66 1016.25 77.0 485.03 480.3355565472517
7.69 36.24 1013.08 88.37 482.46 479.7315598782737
7.7 40.35 1011.72 92.88 484.57 477.91894784424176
7.73 37.8 1020.71 63.93 483.94 483.45191519870116
7.73 39.04 1018.61 68.23 482.39 482.34452319301437
7.76 42.28 1008.52 83.31 483.8 478.45760011663003
7.81 39.64 1011.42 86.68 482.22 478.7638216096892
7.82 40.72 1022.17 88.13 481.52 479.1388800137473
7.84 43.52 1022.23 96.51 483.79 477.1846287648614
7.87 42.85 1012.18 94.21 480.54 476.81117998099177
7.89 40.46 1019.61 74.53 477.27 480.84424359067077
7.9 40.0 1018.74 79.55 474.5 480.1364995691749
7.91 39.96 1023.57 88.44 475.52 479.2235127059344
7.92 39.54 1011.51 84.41 481.44 478.91505388939413
7.95 41.26 1008.48 97.92 480.6 476.210862698602
7.97 38.5 1013.84 72.36 487.19 481.0254321330136
8.01 40.46 1019.42 76.15 475.42 480.36098930984286
8.01 40.62 1015.62 86.43 476.22 478.51210495606927
8.01 41.66 1014.49 76.72 485.13 479.57731721621883
8.03 40.07 1016.06 47.46 488.02 484.33140555054337
8.04 40.64 1020.64 89.26 477.78 478.4450809561189
8.05 40.62 1018.16 73.67 477.2 480.5031526805204
8.07 43.41 1016.02 76.26 467.56 479.2169410835439
8.07 43.69 1017.05 87.34 485.18 477.6146348725092
8.11 41.92 1029.61 91.92 483.52 478.33312476559934
8.16 39.72 1020.54 82.11 480.21 479.4778900760471
8.18 40.02 1031.45 73.66 478.81 481.48536176155926
8.23 43.79 1016.11 82.11 484.67 477.9675154808001
8.24 39.61 1017.99 78.42 477.9 479.6817134321857
8.25 41.26 1020.59 91.84 475.17 477.5050067316079
8.26 40.96 1025.23 89.22 485.73 478.32045063849523
8.27 39.64 1011.0 84.64 479.91 478.1399555772117
8.28 40.56 1023.29 79.44 486.4 479.65037308403333
8.28 40.77 1011.55 89.79 480.15 477.1324329554068
8.3 36.3 1015.97 60.62 480.58 482.82343628916425
8.3 43.13 1020.02 83.11 484.07 478.16947013024355
8.33 38.08 1018.94 73.78 481.89 480.6437911412516
8.34 40.72 1023.62 83.75 483.14 478.8928744938167
8.35 43.52 1022.78 97.34 479.31 476.1246111330079
8.35 43.79 1016.2 85.23 484.21 477.288235770853
8.37 40.92 1021.82 86.03 476.02 478.30600580479216
8.39 36.24 1013.39 89.13 480.69 478.2957350932866
8.42 42.28 1008.4 87.78 481.91 476.52270993699136
8.46 40.8 1023.57 81.27 485.06 478.99917896902446
8.48 38.5 1013.5 66.51 485.29 480.8674382556021
8.51 38.5 1013.33 64.28 482.39 481.1210453702997
8.61 43.8 1021.9 74.35 478.25 478.8354389662744
8.63 39.96 1024.39 99.47 475.79 476.29244393984663
8.65 40.56 1023.23 78.85 485.87 479.0178844308566
8.67 40.77 1011.81 89.4 479.23 476.4582419334783
8.68 41.82 1032.83 73.62 478.61 480.1903466285615
8.72 36.25 1029.31 85.73 479.94 479.4487267515188
8.73 36.18 1013.66 77.74 479.25 479.3384367489267
8.73 41.92 1029.41 89.72 480.99 477.44189376811596
8.74 40.03 1016.81 93.37 481.07 476.33561360755584
8.75 36.3 1015.61 57.53 480.4 482.3769169335795
8.75 40.22 1008.96 90.53 482.8 476.04420182940044
8.76 41.82 1033.29 76.5 480.08 479.65335282852305
8.77 40.46 1019.68 77.84 475.0 478.6696951676176
8.8 42.32 1017.91 86.8 483.88 476.696926422392
8.81 43.56 1014.99 81.5 482.52 476.90393713153463
8.83 36.25 1028.86 85.6 478.45 479.2188844607746
8.85 40.22 1008.61 90.6 482.3 475.81261283946816
8.87 41.16 1023.17 94.13 472.45 476.2100211409317
8.88 36.66 1026.61 76.16 480.2 480.2141595165933
8.91 43.52 1022.78 98.0 478.38 474.9481764100585
8.93 40.46 1019.03 71.0 472.77 479.30598211413803
8.94 41.74 1022.55 90.74 483.53 476.3744577455605
8.96 40.02 1031.21 82.32 475.47 478.6980048877349
8.96 40.05 1015.91 89.4 467.24 476.41215657483474
8.99 36.66 1028.11 71.98 481.03 480.7338755379764
9.01 38.56 1016.67 63.61 482.37 480.51130475015583
9.03 40.71 1025.98 81.94 484.97 478.0206284049
9.04 44.68 1023.14 90.73 479.53 475.4980717304681
9.06 43.79 1016.05 81.32 482.8 476.4769333498689
9.08 36.18 1020.24 68.37 477.26 480.5658974037096
9.08 36.71 1025.07 81.32 479.02 478.9378167534134
9.08 40.02 1031.2 75.34 476.69 479.483969889582
9.11 40.64 1020.68 86.91 476.62 476.72728884411293
9.12 41.49 1020.58 96.23 475.69 475.1283411969007
9.12 41.54 1018.61 79.26 482.95 477.43108128919135
9.13 39.04 1022.36 74.6 483.24 479.0201634085648
9.13 39.16 1014.14 86.17 484.86 476.63324412261045
9.14 37.36 1015.22 78.06 491.97 478.3337308000573
9.15 39.61 1018.11 75.29 474.88 478.39283560851754
9.15 39.61 1018.69 84.47 475.64 477.10087603877
9.16 41.03 1021.3 76.08 484.96 478.16396362897893
9.19 40.62 1017.78 68.91 475.42 478.96772021173297
9.2 40.03 1017.05 92.46 480.38 475.6006326388746
9.26 44.68 1023.22 91.44 478.82 474.9766634864767
9.29 39.04 1022.72 74.29 482.55 478.786077505979
9.3 38.18 1017.19 71.51 480.14 478.9365615888623
9.31 43.56 1015.09 79.96 482.55 476.1723094438278
9.32 37.73 1022.14 79.49 477.91 478.2490255806092
9.35 44.03 1011.02 88.11 476.25 474.4577268339357
9.37 40.11 1024.94 75.03 471.13 478.43777544278043
9.39 39.66 1019.22 75.33 482.16 478.00197412694763
9.41 34.69 1027.02 78.91 480.87 479.3152324207446
9.41 39.61 1016.14 78.38 477.34 477.2801935898294
9.45 39.04 1023.08 75.81 483.66 478.285031656868
9.46 42.28 1008.67 78.16 481.95 475.9420528274367
9.47 41.4 1026.49 87.9 479.68 476.17198214059135
9.48 44.68 1023.29 92.9 478.66 474.34503134979343
9.5 37.36 1015.13 63.41 478.8 479.7691576930105
9.51 43.79 1016.02 79.81 481.12 475.82678857769963
9.53 38.18 1018.43 75.33 476.54 478.0366119605891
9.53 38.38 1020.49 80.08 478.03 477.46151999839185
9.55 38.08 1019.34 68.38 479.23 479.110912113517
9.55 39.66 1018.8 74.88 480.74 477.72481326338215
9.59 34.69 1027.65 75.32 478.88 479.54303529435083
9.59 38.56 1017.52 61.89 481.05 479.71268358186353
9.61 44.03 1008.3 91.36 473.54 473.26068816423447
9.63 41.14 1027.99 87.89 469.73 476.05175958076205
9.68 38.16 1015.54 79.67 475.51 476.88388448180046
9.68 41.06 1022.75 87.44 476.67 475.61433131158714
9.69 40.46 1019.1 71.91 472.16 477.7130066863276
9.72 41.44 1015.17 84.41 481.85 475.2673812565115
9.75 36.66 1026.21 70.12 479.45 479.3846135509556
9.76 39.16 1014.19 85.4 482.38 475.5344685772051
9.78 52.75 1022.97 78.31 469.58 473.85672752722735
9.82 39.64 1010.79 69.22 477.93 477.3826135030455
9.83 41.17 1019.34 72.29 478.21 477.2300569616709
9.84 36.66 1026.7 70.02 477.62 479.265495182268
9.86 37.83 1005.05 100.15 472.46 472.7773808573311
9.86 41.01 1018.49 98.71 467.37 473.2887424901299
9.87 40.81 1017.17 84.25 473.2 475.32128019247375
9.88 39.66 1017.81 76.05 480.05 476.83702080523557
9.92 40.64 1020.39 95.41 469.65 473.90133694043874
9.93 40.67 1018.08 69.74 485.23 477.4312500724956
9.95 43.56 1014.85 82.62 477.88 474.53026960482526
9.96 41.26 1022.9 83.83 475.21 475.5632280329792
9.96 41.55 1002.59 65.86 475.91 476.45899184845075
9.96 42.03 1017.78 82.39 477.85 475.16451288467897
9.97 41.62 1013.99 96.02 464.86 472.95056743270436
9.98 41.01 1017.83 98.07 466.05 473.09691475779204
9.98 41.62 1013.56 95.77 463.16 472.93274352761154
9.99 41.82 1033.14 68.36 475.75 478.4561215361668
9.99 42.07 1018.78 85.68 471.6 474.6981382928799
10.01 40.78 1023.71 88.11 470.82 475.02803269176394
10.02 41.44 1017.37 77.65 479.63 475.85397168574394
10.06 34.69 1027.9 71.73 477.68 479.18053767427625
10.08 43.14 1010.67 85.9 478.73 473.5654621009553
10.09 37.14 1012.99 72.59 473.66 477.1725989266351
10.1 41.4 1024.29 85.94 474.28 475.0636358283201
10.1 41.58 1021.26 94.06 468.19 473.5875494551498
10.11 41.62 1017.17 97.82 463.57 472.67682233352394
10.12 41.55 1005.78 62.34 475.46 476.9235641778536
10.12 43.72 1011.33 97.62 473.05 471.68772310073444
10.13 38.16 1013.57 79.04 474.92 475.9474342905188
10.15 41.46 1019.78 83.56 481.31 474.932278891215
10.15 43.41 1018.4 82.07 473.43 474.55112952359036
10.16 41.55 1005.69 58.04 477.27 477.46636654211125
10.18 43.5 1022.84 88.7 476.91 473.8650937482109
10.2 41.01 1021.39 96.64 468.27 473.17098851617544
10.2 41.46 1019.12 83.26 480.11 474.8258713039414
10.22 37.83 1005.94 93.53 471.79 473.1211730372522
10.23 41.46 1020.45 84.95 480.87 474.62974157133897
10.24 39.28 1010.13 81.61 477.53 474.801072598715
10.25 41.26 1007.44 98.08 476.03 471.6665106288944
10.25 41.46 1018.67 84.41 479.28 474.5250337253637
10.27 52.75 1026.19 76.78 470.76 473.3969220129792
10.28 39.64 1010.45 74.15 477.65 475.74847822607404
10.31 37.5 1008.55 99.31 474.16 472.3991408528041
10.31 38.18 1018.02 70.1 476.31 477.2616855096003
10.32 38.91 1012.11 81.49 479.17 474.9177051337058
10.33 40.62 1017.41 64.22 473.16 477.4228902388337
10.34 41.46 1017.75 89.73 478.03 473.50046202531144
10.37 37.83 1006.5 90.99 470.66 473.24796901874475
10.39 40.22 1006.59 87.77 479.14 473.09058493481064
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20.08 62.52 1017.99 75.74 450.98 451.5232844413466
20.1 57.17 1011.96 87.68 452.67 450.58585768893414
20.11 51.19 1007.82 92.06 449.03 451.0815006280822
20.12 58.12 1015.47 79.38 453.33 451.80697350389795
20.16 57.76 1019.34 72.1 455.13 453.19662660647845
20.19 44.57 1009.2 72.13 454.36 455.59739505823774
20.19 66.86 1012.97 64.7 454.84 451.430922332517
20.21 54.9 1016.82 66.56 454.23 454.4162557726164
20.21 69.94 1009.33 83.96 447.06 447.51848167260005
20.23 52.05 1012.15 47.49 457.57 457.4899833309971
20.24 56.65 1020.72 62.9 455.49 454.7734968589603
20.25 44.78 1007.93 40.16 462.44 459.9896954813451
20.28 48.78 1017.4 82.51 451.59 453.5274885789799
20.28 62.52 1017.89 75.67 452.45 451.13958592197287
20.3 58.46 1015.93 82.13 448.79 451.01129177115814
20.33 57.76 1016.47 75.35 450.25 452.16097295111786
20.37 52.05 1012.34 62.57 456.11 455.0355456385039
20.43 63.09 1016.46 91.78 445.58 448.2416124360999
20.45 59.8 1015.13 79.21 452.96 450.748723139911
20.5 49.69 1009.6 70.81 452.94 453.9480760674323
20.51 39.72 1002.25 47.97 452.39 459.1480198621134
20.51 68.08 1012.73 78.05 445.96 448.54249260358677
20.56 60.08 1017.79 78.08 452.8 450.84813038147
20.56 64.45 1012.24 53.09 458.97 452.9523382793844
20.58 39.53 1005.68 62.09 460.1 457.2797775931854
20.59 59.8 1015.27 77.94 453.83 450.67534900317014
20.6 59.15 1013.32 91.07 443.76 448.7439698617689
20.61 60.1 1010.84 80.57 450.46 449.81767655065505
20.61 62.91 1013.24 79.54 446.53 449.46273191454395
20.61 63.86 1015.43 73.86 446.34 450.23276134284185
20.61 65.61 1014.91 83.82 449.72 448.3011628860887
20.64 61.86 1012.81 99.97 447.14 446.65132022669536
20.65 41.67 1012.76 45.27 455.5 459.6412858596406
20.65 57.5 1016.04 87.45 448.22 449.8084139802319
20.68 63.86 1015.73 74.36 447.84 450.0492245621943
20.69 50.78 1008.71 91.95 447.58 450.1534891767275
20.71 58.18 1007.63 98.44 447.06 447.2352893702614
20.72 63.94 1017.17 59.83 447.69 452.1889854808296
20.76 44.58 1017.09 57.47 462.01 457.2763644769643
20.76 59.04 1012.51 85.39 448.92 449.22543586447597
20.76 69.05 1001.89 77.86 442.82 446.9636998742384
20.77 43.77 1010.76 63.12 453.46 456.1194901357003
20.78 58.86 1016.02 77.29 446.2 450.6991034809647
20.8 69.45 1013.7 82.48 443.77 447.0742816761689
20.82 63.77 1014.28 86.37 448.9 447.93156732156075
20.83 44.78 1008.51 35.9 460.6 459.53962876300176
20.85 59.21 1012.9 75.97 444.44 450.41539217977487
20.87 57.19 1006.5 77.0 445.95 450.2091683575852
20.88 59.8 1015.66 75.34 453.18 450.5270199129538
20.9 67.07 1005.43 82.85 443.46 446.7475520504839
20.92 70.02 1010.23 95.58 444.64 444.5071996193093
20.94 44.78 1008.14 35.7 465.57 459.3265106832487
20.94 68.12 1012.43 78.2 446.41 447.6568115900025
20.95 44.89 1010.48 67.97 450.39 454.7627521673882
20.95 48.14 1013.3 67.72 452.38 454.2185133216816
20.95 70.72 1009.96 87.73 445.66 445.39798853968557
20.96 69.48 1011.04 82.63 444.31 446.5197598700026
20.98 60.1 1011.07 79.44 450.95 449.2875712413884
20.99 67.07 1005.17 82.41 442.02 446.61697690688305
21.01 58.96 1014.33 61.8 453.88 452.35263519535
21.06 50.59 1016.42 66.12 454.15 453.8829146493028
21.06 62.91 1011.92 75.52 455.22 449.0737291884585
21.06 67.07 1004.9 84.09 446.44 446.2148995417375
21.08 44.05 1008.13 72.52 449.6 453.8663667211949
21.11 58.66 1011.7 68.71 457.89 451.0124137711604
21.11 59.39 1013.87 85.29 446.02 448.5883814092364
21.13 51.43 1007.43 88.72 445.4 449.5097304398748
21.14 58.05 1012.98 87.27 449.74 448.5033053489824
21.14 58.98 1009.05 94.36 448.31 446.9172206057899
21.16 45.38 1014.65 73.06 458.63 453.8324720669487
21.18 44.57 1007.27 73.67 449.93 453.30606496401464
21.18 60.1 1011.02 78.19 452.39 449.08008122232087
21.19 74.93 1015.75 80.84 443.29 445.36190546480196
21.26 50.32 1008.41 87.22 446.22 449.83432112958235
21.28 70.32 1011.06 90.12 439.0 444.60209183702824
21.32 45.01 1012.23 59.94 452.75 455.3330390953461
21.32 49.02 1008.81 85.81 445.65 450.28095549994475
21.33 58.46 1016.17 79.73 445.27 449.3942290809264
21.33 60.1 1010.97 78.36 451.95 448.76188428428145
21.33 63.86 1020.33 72.13 445.02 449.49526226001734
21.34 59.8 1016.92 77.06 450.74 449.4914112072868
21.36 58.95 1018.35 78.87 443.93 449.5171242611325
21.36 68.28 1007.6 72.37 445.91 447.2640843894497
21.38 44.05 1005.69 81.66 445.71 451.75573664689705
21.39 51.3 1013.39 89.05 452.7 449.47769197848703
21.39 63.9 1013.44 70.95 449.38 448.9807967933042
21.4 44.57 1005.7 73.09 445.09 452.83851864090616
21.4 68.28 1008.2 60.34 450.22 448.99070865761826
21.41 56.9 1007.03 79.41 441.41 448.9314689751323
21.42 43.79 1015.76 43.08 462.19 458.19122302999057
21.44 51.19 1009.1 84.94 446.17 449.6590005617065
21.44 63.09 1016.56 90.11 444.19 446.545237358833
21.45 45.09 1013.83 52.26 453.15 456.3129527756159
21.45 60.08 1017.92 72.7 451.49 449.9268730104538
21.45 66.05 1014.81 73.73 453.38 448.0350183689646
21.46 46.63 1012.97 71.29 452.1 453.06361431502756
21.47 58.79 1017.0 76.97 446.33 449.51211271813366
21.49 56.9 1007.47 66.66 452.46 450.672947544889
21.52 66.51 1015.32 72.85 444.87 447.9552056621943
21.53 52.84 1005.06 88.22 444.04 448.2666586698033
21.54 58.49 1010.85 78.9 449.12 448.66968221055265
21.54 69.48 1011.04 80.48 443.15 445.7146703190735
21.55 44.57 1006.03 71.54 446.84 452.8021698612517
21.55 60.27 1017.42 92.59 443.93 446.7443660008083
21.57 66.49 1014.76 68.19 455.18 448.49796091182566
21.58 63.87 1015.27 63.15 451.88 449.90863388073797
21.61 41.54 1014.5 75.62 458.47 453.53620348431275
21.61 49.39 1019.41 78.2 449.26 451.60241101975805
21.62 50.05 1007.2 92.9 444.16 448.28015134415546
21.65 58.18 1008.33 95.28 441.05 445.940139097113
21.69 44.57 1005.84 71.53 447.88 452.5181226448485
21.71 65.27 1013.24 63.58 444.91 449.080854354919
21.73 69.05 1001.31 86.64 439.01 443.764677908941
21.75 59.8 1016.65 72.94 453.17 449.2796285666682

Now that we have real predictions we can use an evaluation metric such as Root Mean Squared Error to validate our regression model. The lower the Root Mean Squared Error, the better our model.

//Now let's compute some evaluation metrics against our test dataset

import org.apache.spark.mllib.evaluation.RegressionMetrics 

val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
import org.apache.spark.mllib.evaluation.RegressionMetrics
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@2cb97126
val rmse = metrics.rootMeanSquaredError
rmse: Double = 4.426730543741181
val explainedVariance = metrics.explainedVariance
explainedVariance: Double = 269.3177257387032
val r2 = metrics.r2
r2: Double = 0.9314313152952873
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")  
println (f"R2: $r2")
Root Mean Squared Error: 4.426730543741181
Explained Variance: 269.3177257387032
R2: 0.9314313152952873

Generally a good model will have 68% of predictions within 1 RMSE and 95% within 2 RMSE of the actual value. Let's calculate and see if our RMSE meets this criteria.

display(predictionsAndLabels) // recall the DataFrame predictionsAndLabels
// First we calculate the residual error and divide it by the RMSE from predictionsAndLabels DataFrame and make another DataFrame that is registered as a temporary table Power_Plant_RMSE_Evaluation
predictionsAndLabels.selectExpr("PE", "Predicted_PE", "PE - Predicted_PE AS Residual_Error", s""" (PE - Predicted_PE) / $rmse AS Within_RSME""").createOrReplaceTempView("Power_Plant_RMSE_Evaluation")
SELECT * from Power_Plant_RMSE_Evaluation
PE Predicted_PE Residual_Error Within_RSME
490.34 493.23742177145215 -2.897421771452173 -0.6545286058914855
482.66 488.93663844863084 -6.27663844863082 -1.4178948518800556
489.04 488.2642491377776 0.7757508622224236 0.17524239493619823
489.64 487.68500173970443 1.9549982602955538 0.44163480044197995
489.0 487.8432005878593 1.1567994121406855 0.26132139752130357
488.03 486.7875685475632 1.24243145243679 0.28066570579802425
491.9 485.8682911927764 6.031708807223595 1.3625651590092023
489.36 485.34119715268844 4.018802847311576 0.9078489886839033
481.47 482.9938172155284 -1.5238172155283678 -0.3442308494884213
482.05 482.5648390621137 -0.5148390621137082 -0.11630232674577932
494.75 487.00024243767876 7.749757562321236 1.7506729821805804
482.98 483.3467525779819 -0.3667525779818561 -8.284953745386567e-2
475.34 482.8336530053327 -7.493653005332703 -1.692818871916148
483.73 483.6145343498689 0.11546565013111376 2.608373132048146e-2
488.42 484.53187599470635 3.888124005293662 0.8783285919200484
495.35 487.2657538698361 8.084246130163933 1.8262340682999108
491.32 487.10787889447494 4.212121105525057 0.9515196517846441
486.46 482.0547750131424 4.405224986857604 0.9951418870719423
483.12 483.688095258955 -0.5680952589549975 -0.12833292050229034
495.23 487.0194077282659 8.210592271734129 1.8547757064958097
479.91 480.1986449575354 -0.2886449575353822 -6.520499829010114e-2
492.12 484.35541367676683 7.764586323233175 1.7540228045303743
491.38 483.7973981417879 7.582601858212115 1.712912449331917
481.56 484.07023605498495 -2.5102360549849436 -0.567063215206105
491.84 484.05572584065357 7.78427415934641 1.758470293691663
484.64 483.0334383183464 1.6065616816536021 0.36292285373571487
490.07 483.9952002249004 6.0747997750996205 1.3722994239368362
478.02 480.02034741363497 -2.0003474136349837 -0.4518791902667791
476.61 479.7602256710553 -3.1502256710552956 -0.711637096481805
487.09 483.7798894431585 3.3101105568414937 0.7477551488923485
482.82 483.2217349280458 -0.401734928045812 -9.075206274161249e-2
481.6 480.1171178824119 1.482882117588133 0.33498359634397323
470.02 482.0478125504672 -12.027812550467218 -2.717087121436152
489.05 481.81327159305386 7.236728406946156 1.6347795140090344
487.03 484.6333949755666 2.39660502443337 0.5413939250993844
490.57 482.2826790358646 8.287320964135404 1.8721087453250556
488.57 482.86050781997415 5.709492180025848 1.2897763086344445
480.39 483.17821215232124 -2.7882121523212504 -0.6298581141929721
481.37 483.0829476732433 -1.7129476732433204 -0.3869554869711247
489.62 482.71830544679335 6.901694553206653 1.5590952476122018
481.13 481.35862683823984 -0.22862683823984753 -5.164688385271067e-2
485.94 481.4164995749685 4.5235004250315 1.021860350507925
482.49 482.3037528502627 0.18624714973731216 4.2073297187840204e-2
483.77 483.6070229944035 0.1629770055964741 3.6816563372465104e-2
491.77 481.0249164343975 10.7450835656025 2.4273181887690556
483.43 482.2081944229683 1.2218055770317164 0.2760063132279849
481.49 482.6905244198958 -1.2005244198957712 -0.2711988922825122
478.78 480.9741288614041 -2.194128861404124 -0.49565448805243767
492.96 485.8565717694332 7.103428230566806 1.6046669568831393
481.36 483.99243959507345 -2.6324395950734356 -0.5946690382578993
486.68 481.76650494734884 4.913495052651172 1.1099602752189677
484.94 483.069724719673 1.8702752803270073 0.42249584921569994
483.01 481.33834961288795 1.671650387112038 0.3776264153858503
488.17 483.1883946104104 4.9816053895896175 1.1253464244922151
490.41 480.1679106982307 10.24208930176934 2.313691606156222
483.11 482.4149038683481 0.6950961316518942 0.15702246269194528
485.89 480.94068220101354 4.949317798986442 1.1180526463224945
487.08 482.508645049916 4.571354950083958 1.0326707046913566
477.8 480.01746628251317 -2.2174662825131577 -0.5009264197587914
489.96 480.63940670945976 9.320593290540216 2.1055253303633577
486.92 483.0108446487773 3.9091553512226938 0.883079580425271
486.37 482.57972730795177 3.7902726920482337 0.8562239455498787
488.2 483.51586402481416 4.684135975185825 1.0581479782654901
479.06 481.6634377951154 -2.603437795115383 -0.5881175213603873
480.05 484.68450470880435 -4.634504708804343 -1.0469362575856187
487.18 482.8218608903564 4.358139109643616 0.9845051707078616
480.47 481.8880244206695 -1.4180244206694965 -0.32033221960491765
480.36 480.6306788292839 -0.2706788292838951 -6.11464435454739e-2
487.85 479.89671820679695 7.953281793203075 1.7966491781271794
486.11 479.6914116057231 6.418588394276924 1.449961394951398
481.83 481.1970697561745 0.6329302438254558 0.1429791665816065
482.96 482.17217802621536 0.7878219737846166 0.17796926331974147
483.92 481.3074409763495 2.612559023650533 0.590178010121793
477.69 479.64992318380445 -1.9599231838044489 -0.4427473424095181
474.25 480.70714895561014 -6.457148955610137 -1.4586722394340677
479.08 479.7024675196716 -0.6224675196716021 -0.14061563348410486
478.89 481.00544489343537 -2.115444893435381 -0.4778797517789611
483.11 482.38901084355183 0.7209891564481836 0.16287170617772706
488.43 481.25646633043965 7.173533669560356 1.6205038004183012
483.28 480.33371483717946 2.946285162820516 0.6655668633335223
486.76 482.5311759738776 4.228824026122368 0.9552928474721311
476.58 480.67721106043905 -4.097211060439065 -0.9255614318409298
481.61 482.53257783094546 -0.9225778309454427 -0.20841065925050425
477.8 480.0330510466423 -2.233051046642288 -0.5044470235035043
475.32 479.3324132109004 -4.012413210900434 -0.9064055675522111
475.89 478.2499419685471 -2.359941968547105 -0.5331117277702286
485.03 480.3355565472517 4.69444345274826 1.0604764411029242
482.46 479.7315598782737 2.728440121726294 0.6163555912803304
484.57 477.91894784424176 6.6510521557582365 1.502475041125319
483.94 483.45191519870116 0.48808480129883947 0.11025852973791857
482.39 482.34452319301437 4.547680698561862e-2 1.027322682875218e-2
483.8 478.45760011663003 5.342399883369978 1.2068500286116204
482.22 478.7638216096892 3.4561783903108108 0.7807519242835766
481.52 479.1388800137473 2.3811199862526564 0.5378958494817917
483.79 477.1846287648614 6.605371235138648 1.4921557049542535
480.54 476.81117998099177 3.7288200190082534 0.8423417649127792
477.27 480.84424359067077 -3.5742435906707897 -0.8074228949228237
474.5 480.1364995691749 -5.636499569174873 -1.273287251952606
475.52 479.2235127059344 -3.703512705934429 -0.8366248339128552
481.44 478.91505388939413 2.5249461106058675 0.5703862219885535
480.6 476.210862698602 4.389137301398023 0.9915076732202934
487.19 481.0254321330136 6.164567866986374 1.392578067734949
475.42 480.36098930984286 -4.940989309842848 -1.1161712376708273
476.22 478.51210495606927 -2.292104956069238 -0.5177873225895746
485.13 479.57731721621883 5.552682783781165 1.2543530104022107
488.02 484.33140555054337 3.688594449456616 0.8332547944829863
477.78 478.4450809561189 -0.6650809561189135 -0.15024202389261102
477.2 480.5031526805204 -3.303152680520384 -0.7461833621634392
467.56 479.2169410835439 -11.65694108354387 -2.6333071255094267
485.18 477.6146348725092 7.565365127490793 1.7090186657480726
483.52 478.33312476559934 5.186875234400645 1.17171695524459
480.21 479.4778900760471 0.7321099239528621 0.16538389150158914
478.81 481.48536176155926 -2.675361761559259 -0.6043651708916123
484.67 477.9675154808001 6.702484519199913 1.5140936302699408
477.9 479.6817134321857 -1.781713432185711 -0.40248969630754267
475.17 477.5050067316079 -2.335006731607905 -0.5274788489010923
485.73 478.32045063849523 7.409549361504787 1.673819828943716
479.91 478.1399555772117 1.7700444227883168 0.3998536629456538
486.4 479.65037308403333 6.749626915966644 1.5247431144210337
480.15 477.1324329554068 3.017567044593193 0.681669465709775
480.58 482.82343628916425 -2.243436289164265 -0.5067930534728822
484.07 478.16947013024355 5.90052986975644 1.3329317905059794
481.89 480.6437911412516 1.246208858748389 0.2815190232236669
483.14 478.8928744938167 4.247125506183295 0.9594271583094607
479.31 476.1246111330079 3.1853888669921275 0.7195804749163808
484.21 477.288235770853 6.921764229146959 1.563628994525865
476.02 478.30600580479216 -2.2860058047921825 -0.5164095221527084
480.69 478.2957350932866 2.3942649067133743 0.5408652916763936
481.91 476.52270993699136 5.387290063008663 1.2169907361146226
485.06 478.99917896902446 6.060821030975546 1.3691416206809235
485.29 480.8674382556021 4.42256174439791 0.9990582667496749
482.39 481.1210453702997 1.2689546297002607 0.2866573009496584
478.25 478.8354389662744 -0.5854389662744097 -0.1322508701375881
475.79 476.29244393984663 -0.5024439398466143 -0.11350226422907182
485.87 479.0178844308566 6.852115569143393 1.5478953375265612
479.23 476.4582419334783 2.771758066521727 0.6261411303745675
478.61 480.1903466285615 -1.5803466285614718 -0.3570008639436786
479.94 479.4487267515188 0.4912732484812068 0.11097880108736301
479.25 479.3384367489267 -8.84367489267106e-2 -1.997789295120947e-2
480.99 477.44189376811596 3.5481062318840486 0.8015184563019332
481.07 476.33561360755584 4.734386392444151 1.0694995653480546
480.4 482.3769169335795 -1.9769169335795027 -0.44658623651141477
482.8 476.04420182940044 6.755798170599576 1.5261372030315674
480.08 479.65335282852305 0.42664717147692954 9.637974736911713e-2
475.0 478.6696951676176 -3.66969516761759 -0.8289854400119429
483.88 476.696926422392 7.183073577608013 1.6226588690300885
482.52 476.90393713153463 5.616062868465349 1.2686705940133918
478.45 479.2188844607746 -0.7688844607745864 -0.17369127241360752
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447.69 452.1889854808296 -4.498985480829617 -1.016322415917227
462.01 457.2763644769643 4.733635523035673 1.069329943682345
448.92 449.22543586447597 -0.30543586447595317 -6.899807012373943e-2
442.82 446.9636998742384 -4.1436998742383935 -0.9360632713678595
453.46 456.1194901357003 -2.6594901357003096 -0.600779764980383
446.2 450.6991034809647 -4.49910348096472 -1.0163490721896018
443.77 447.0742816761689 -3.3042816761688982 -0.7464384026808953
448.9 447.93156732156075 0.9684326784392283 0.21876928556414296
460.6 459.53962876300176 1.0603712369982645 0.23953823855339262
444.44 450.41539217977487 -5.975392179774872 -1.349843212892932
445.95 450.2091683575852 -4.259168357585224 -0.9621476427127764
453.18 450.5270199129538 2.652980087046217 0.599309142680298
443.46 446.7475520504839 -3.287552050483896 -0.7426591743046266
444.64 444.5071996193093 0.13280038069069633 2.999965310254963e-2
465.57 459.3265106832487 6.2434893167512655 1.4104064512304106
446.41 447.6568115900025 -1.246811590002494 -0.281655180427759
450.39 454.7627521673882 -4.372752167388228 -0.9878062656356458
452.38 454.2185133216816 -1.838513321681603 -0.4153208114916822
445.66 445.39798853968557 0.2620114603144543 5.918848182094669e-2
444.31 446.5197598700026 -2.2097598700025856 -0.4991855384391755
450.95 449.2875712413884 1.662428758611611 0.37554324623667645
442.02 446.61697690688305 -4.5969769068830715 -1.0384587138204282
453.88 452.35263519535 1.5273648046500057 0.34503225112933517
454.15 453.8829146493028 0.26708535069718664 6.033467545812348e-2
455.22 449.0737291884585 6.146270811541513 1.388444756419958
446.44 446.2148995417375 0.2251004582624887 5.085027336501233e-2
449.6 453.8663667211949 -4.266366721194856 -0.9637737556054642
457.89 451.0124137711604 6.8775862288395615 1.553649168586412
446.02 448.5883814092364 -2.5683814092363946 -0.5801982713557641
445.4 449.5097304398748 -4.109730439874795 -0.9283895640961064
449.74 448.5033053489824 1.2366946510176149 0.2793697603225793
448.31 446.9172206057899 1.3927793942100948 0.3146293591732849
458.63 453.8324720669487 4.797527933051299 1.0837632617676214
449.93 453.30606496401464 -3.376064964014631 -0.7626542728669009
452.39 449.08008122232087 3.3099187776791155 0.7477118259115428
443.29 445.36190546480196 -2.0719054648019437 -0.4680441794071581
446.22 449.83432112958235 -3.614321129582322 -0.8164764251785102
439.0 444.60209183702824 -5.60209183702824 -1.2655145330562003
452.75 455.3330390953461 -2.5830390953461233 -0.5835094478470579
445.65 450.28095549994475 -4.6309554999447755 -1.046134490045332
445.27 449.3942290809264 -4.124229080926398 -0.9316648122523561
451.95 448.76188428428145 3.1881157157185385 0.7201964710108949
445.02 449.49526226001734 -4.4752622600173595 -1.0109633319210711
450.74 449.4914112072868 1.2485887927132353 0.2820566511504923
443.93 449.5171242611325 -5.587124261132487 -1.262133352352325
445.91 447.2640843894497 -1.3540843894496675 -0.30588814387272023
445.71 451.75573664689705 -6.045736646897069 -1.3657340529671387
452.7 449.47769197848703 3.2223080215129585 0.7279205250179235
449.38 448.9807967933042 0.3992032066958018 9.01801459906393e-2
445.09 452.83851864090616 -7.748518640906184 -1.7503931093934726
450.22 448.99070865761826 1.2292913423817708 0.27769735027577597
441.41 448.9314689751323 -7.521468975132279 -1.6991025093602443
462.19 458.19122302999057 3.9987769700094304 0.9033251358981357
446.17 449.6590005617065 -3.4890005617064617 -0.7881664644439343
444.19 446.545237358833 -2.3552373588329942 -0.532048954767078
453.15 456.3129527756159 -3.1629527756159064 -0.7145121539163726
451.49 449.9268730104538 1.5631269895462196 0.35311094138229787
453.38 448.0350183689646 5.3449816310354095 1.2074332463249917
452.1 453.06361431502756 -0.9636143150275416 -0.21768081556036123
446.33 449.51211271813366 -3.1821127181336806 -0.718840391727202
452.46 450.672947544889 1.7870524551109952 0.40369578348012486
444.87 447.9552056621943 -3.085205662194312 -0.6969490534174008
444.04 448.2666586698033 -4.226658669803271 -0.9548036927115915
449.12 448.66968221055265 0.4503177894473538 0.10172694836464449
443.15 445.7146703190735 -2.564670319073514 -0.5793599347716393
446.84 452.8021698612517 -5.962169861251709 -1.3468562864485707
443.93 446.7443660008083 -2.8143660008083202 -0.6357662778430159
455.18 448.49796091182566 6.682039088174349 1.5094750001492365
451.88 449.90863388073797 1.9713661192620293 0.44533230558821424
458.47 453.53620348431275 4.933796515687277 1.1145463829197875
449.26 451.60241101975805 -2.3424110197580603 -0.5291514802205262
444.16 448.28015134415546 -4.120151344155431 -0.9307436500694146
441.05 445.940139097113 -4.890139097112979 -1.1046841565784926
447.88 452.5181226448485 -4.638122644848522 -1.047753550621287
444.91 449.080854354919 -4.1708543549189585 -0.9421974781853397
439.01 443.764677908941 -4.754677908941005 -1.0740834261221295
453.17 449.2796285666682 3.890371433331836 0.8788362867110385
-- Now we can display the RMSE as a Histogram. Clearly this shows that the RMSE is centered around 0 with the vast majority of the error within 2 RMSEs.
SELECT Within_RSME  from Power_Plant_RMSE_Evaluation
Within_RSME
-0.6545286058914855
-1.4178948518800556
0.17524239493619823
0.44163480044197995
0.26132139752130357
0.28066570579802425
1.3625651590092023
0.9078489886839033
-0.3442308494884213
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1.7506729821805804
-8.284953745386567e-2
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2.608373132048146e-2
0.8783285919200484
1.8262340682999108
0.9515196517846441
0.9951418870719423
-0.12833292050229034
1.8547757064958097
-6.520499829010114e-2
1.7540228045303743
1.712912449331917
-0.567063215206105
1.758470293691663
0.36292285373571487
1.3722994239368362
-0.4518791902667791
-0.711637096481805
0.7477551488923485
-9.075206274161249e-2
0.33498359634397323
-2.717087121436152
1.6347795140090344
0.5413939250993844
1.8721087453250556
1.2897763086344445
-0.6298581141929721
-0.3869554869711247
1.5590952476122018
-5.164688385271067e-2
1.021860350507925
4.2073297187840204e-2
3.6816563372465104e-2
2.4273181887690556
0.2760063132279849
-0.2711988922825122
-0.49565448805243767
1.6046669568831393
-0.5946690382578993
1.1099602752189677
0.42249584921569994
0.3776264153858503
1.1253464244922151
2.313691606156222
0.15702246269194528
1.1180526463224945
1.0326707046913566
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2.1055253303633577
0.883079580425271
0.8562239455498787
1.0581479782654901
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0.9845051707078616
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1.7966491781271794
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0.11025852973791857
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0.8332547944829863
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1.5140936302699408
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1.673819828943716
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0.681669465709775
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1.3329317905059794
0.2815190232236669
0.9594271583094607
0.7195804749163808
1.563628994525865
-0.5164095221527084
0.5408652916763936
1.2169907361146226
1.3691416206809235
0.9990582667496749
0.2866573009496584
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1.5478953375265612
0.6261411303745675
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0.11097880108736301
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0.8015184563019332
1.0694995653480546
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1.5261372030315674
9.637974736911713e-2
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1.6226588690300885
1.2686705940133918
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1.465503060651426
-0.8493901094223798
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0.775250166241454
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1.6164395333609067
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6.689462100697262e-2
0.41987991622217147
1.5698655082870474
0.9108140262190783
1.4283829990671366
-0.7468033961054397
1.8565224554443056e-2
-0.631158789082455
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0.1268789228414677
1.2467256943415417
0.9532625827884375
1.858427070746631
3.080438049074982
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1.5352270267793358
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1.0796607821279804
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0.8502714264690703
0.271857163937664
1.4407225588170043
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0.4048751439362759
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0.9392995195813987
0.3512225476325008
1.3510289270963245e-2
1.214207255223955
1.357197397311141
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1.1957383378087658
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0.12841983400410018
2.6901995797195863e-2
0.6811317532938489
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0.3021002532054435
6.309664277182285e-2
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1.487015909020143
1.4770822031817277e-2
1.5464079765310332
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0.7258131397464832
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1.761740374853143
0.756705284425051
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1.166670943264595
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1.4096765924648265
0.6164656679054759
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0.3977787059312943
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1.3665650089641064
1.746749569478921
0.20876984487810568
1.2521345827220822
0.5034033523179731
0.2428222722587704
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0.529553624374745
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1.1483434385624023
1.7878124840322416
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1.7220135063654567
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2.6840159315323495
1.4048914641301884
1.048062749037262
1.0587197080412232
2.305613395286325
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1.5499716375563712
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1.0232860206712358
7.648152721354506e-2
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1.1080799981170868
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0.6646125250731286
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We can see this definitively if we count the number of predictions within + or - 1.0 and + or - 2.0 and display this as a pie chart:

SELECT case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1  when  Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end RSME_Multiple, COUNT(*) count  from Power_Plant_RMSE_Evaluation
group by case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1  when  Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end
RSME_Multiple count
1.0 1267.0
3.0 77.0
2.0 512.0

So we have about 70% of our training data within 1 RMSE and about 97% (70% + 27%) within 2 RMSE. So the model is pretty decent. Let's see if we can tune the model to improve it further.

NOTE: these numbers will vary across runs due to the seed in random sampling of training and test set, number of iterations, and other stopping rules in optimization, for example.

Step 7: Tuning and Evaluation

Now that we have a model with all of the data let's try to make a better model by tuning over several parameters.

import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._

First let's use a cross validator to split the data into training and validation subsets. See http://spark.apache.org/docs/latest/ml-tuning.html.

//Let's set up our evaluator class to judge the model based on the best root mean squared error
val regEval = new RegressionEvaluator()
regEval.setLabelCol("PE")
  .setPredictionCol("Predicted_PE")
  .setMetricName("rmse")
regEval: org.apache.spark.ml.evaluation.RegressionEvaluator = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
res101: regEval.type = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false

We now treat the lrPipeline as an Estimator, wrapping it in a CrossValidator instance.

This will allow us to jointly choose parameters for all Pipeline stages.

A CrossValidator requires an Estimator, an Evaluator (which we set next).

//Let's create our crossvalidator with 3 fold cross validation
val crossval = new CrossValidator()
crossval.setEstimator(lrPipeline)
crossval.setNumFolds(3)
crossval.setEvaluator(regEval)
crossval: org.apache.spark.ml.tuning.CrossValidator = cv_bc136566b6a6
res102: crossval.type = cv_bc136566b6a6

A CrossValidator also requires a set of EstimatorParamMaps which we set next.

For this we need a regularization parameter (more generally a hyper-parameter that is model-specific).

Now, let's tune over our regularization parameter from 0.01 to 0.10.

val regParam = ((1 to 10) toArray).map(x => (x /100.0))
warning: there was one feature warning; for details, enable `:setting -feature' or `:replay -feature'
regParam: Array[Double] = Array(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1)

Check out the scala docs for syntactic details on org.apache.spark.ml.tuning.ParamGridBuilder.

val paramGrid = new ParamGridBuilder()
                    .addGrid(lr.regParam, regParam)
                    .build()
crossval.setEstimatorParamMaps(paramGrid)
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
	linReg_3b8dffe4015f-regParam: 0.01
}, {
	linReg_3b8dffe4015f-regParam: 0.02
}, {
	linReg_3b8dffe4015f-regParam: 0.03
}, {
	linReg_3b8dffe4015f-regParam: 0.04
}, {
	linReg_3b8dffe4015f-regParam: 0.05
}, {
	linReg_3b8dffe4015f-regParam: 0.06
}, {
	linReg_3b8dffe4015f-regParam: 0.07
}, {
	linReg_3b8dffe4015f-regParam: 0.08
}, {
	linReg_3b8dffe4015f-regParam: 0.09
}, {
	linReg_3b8dffe4015f-regParam: 0.1
})
res103: crossval.type = cv_bc136566b6a6
//Now let's create our model
val cvModel = crossval.fit(trainingSet)
cvModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_07136143d91d, numFolds=3

In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. TrainValidationSplit only evaluates each combination of parameters once as opposed to k times in case of CrossValidator. It is therefore less expensive, but will not produce as reliable results when the training dataset is not sufficiently large.

Now that we have tuned let's see what we got for tuning parameters and what our RMSE was versus our intial model

val predictionsAndLabels = cvModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))

val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@671c9416
rmse: Double = 4.4286032895695895
explainedVariance: Double = 271.5750243564875
r2: Double = 0.9313732865178349
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")  
println (f"R2: $r2")
Root Mean Squared Error: 4.4286032895695895
Explained Variance: 271.5750243564875
R2: 0.9313732865178349

So our initial untuned and tuned linear regression models are statistically identical.

Given that the only linearly correlated variable is Temperature, it makes sense try another machine learning method such a Decision Tree to handle non-linear data and see if we can improve our model

A Decision Tree creates a model based on splitting variables using a tree structure. We will first start with a single decision tree model.

Reference Decision Trees: https://en.wikipedia.org/wiki/Decisiontreelearning

//Let's build a decision tree pipeline
import org.apache.spark.ml.regression.DecisionTreeRegressor

// we are using a Decision Tree Regressor as opposed to a classifier we used for the hand-written digit classification problem
val dt = new DecisionTreeRegressor()
dt.setLabelCol("PE")
dt.setPredictionCol("Predicted_PE")
dt.setFeaturesCol("features")
dt.setMaxBins(100)

val dtPipeline = new Pipeline()
dtPipeline.setStages(Array(vectorizer, dt))
import org.apache.spark.ml.regression.DecisionTreeRegressor
dt: org.apache.spark.ml.regression.DecisionTreeRegressor = dtr_92691f930484
dtPipeline: org.apache.spark.ml.Pipeline = pipeline_ddfeb1cfff04
res106: dtPipeline.type = pipeline_ddfeb1cfff04
//Let's just resuse our CrossValidator
crossval.setEstimator(dtPipeline)
res107: crossval.type = cv_bc136566b6a6
val paramGrid = new ParamGridBuilder()
                     .addGrid(dt.maxDepth, Array(2, 3))
                     .build()
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
	dtr_92691f930484-maxDepth: 2
}, {
	dtr_92691f930484-maxDepth: 3
})
crossval.setEstimatorParamMaps(paramGrid)
res108: crossval.type = cv_bc136566b6a6
val dtModel = crossval.fit(trainingSet) // fit decitionTree with cv
dtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_ddfeb1cfff04, numFolds=3
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel].toDebugString
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
res109: String =
"DecisionTreeRegressionModel: uid=dtr_92691f930484, depth=3, numNodes=15, numFeatures=4
  If (feature 0 <= 18.595)
   If (feature 0 <= 11.885000000000002)
    If (feature 0 <= 8.575)
     Predict: 483.994943457189
    Else (feature 0 > 8.575)
     Predict: 476.04374262101527
   Else (feature 0 > 11.885000000000002)
    If (feature 0 <= 15.475000000000001)
     Predict: 467.5564649956784
    Else (feature 0 > 15.475000000000001)
     Predict: 459.5010376134889
  Else (feature 0 > 18.595)
   If (feature 1 <= 66.21000000000001)
    If (feature 0 <= 22.055)
     Predict: 452.01076923076914
    Else (feature 0 > 22.055)
     Predict: 443.46937926330156
   Else (feature 1 > 66.21000000000001)
    If (feature 0 <= 25.325)
     Predict: 440.73731707317074
    Else (feature 0 > 25.325)
     Predict: 433.86131215469624
"

The line above will pull the Decision Tree model from the Pipeline and display it as an if-then-else string.

Next let's visualize it as a decision tree for regression.

display(dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel])
treeNode
{"index":7,"featureType":"continuous","prediction":null,"threshold":18.595,"categories":null,"feature":0,"overflow":false}
{"index":3,"featureType":"continuous","prediction":null,"threshold":11.885000000000002,"categories":null,"feature":0,"overflow":false}
{"index":1,"featureType":"continuous","prediction":null,"threshold":8.575,"categories":null,"feature":0,"overflow":false}
{"index":0,"featureType":null,"prediction":483.994943457189,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":2,"featureType":null,"prediction":476.04374262101527,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":5,"featureType":"continuous","prediction":null,"threshold":15.475000000000001,"categories":null,"feature":0,"overflow":false}
{"index":4,"featureType":null,"prediction":467.5564649956784,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":6,"featureType":null,"prediction":459.5010376134889,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":11,"featureType":"continuous","prediction":null,"threshold":66.21000000000001,"categories":null,"feature":1,"overflow":false}
{"index":9,"featureType":"continuous","prediction":null,"threshold":22.055,"categories":null,"feature":0,"overflow":false}
{"index":8,"featureType":null,"prediction":452.01076923076914,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":10,"featureType":null,"prediction":443.46937926330156,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":13,"featureType":"continuous","prediction":null,"threshold":25.325,"categories":null,"feature":0,"overflow":false}
{"index":12,"featureType":null,"prediction":440.73731707317074,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":14,"featureType":null,"prediction":433.86131215469624,"threshold":null,"categories":null,"feature":null,"overflow":false}

Now let's see how our DecisionTree model compares to our LinearRegression model

val predictionsAndLabels = dtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)

val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2

println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")  
println (f"R2: $r2")
Root Mean Squared Error: 5.111944542729953
Explained Variance: 261.4355220034205
R2: 0.908560906504635
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@684c4c50
rmse: Double = 5.111944542729953
explainedVariance: Double = 261.4355220034205
r2: Double = 0.908560906504635

So our DecisionTree was slightly worse than our LinearRegression model (LR: 4.6 vs DT: 5.2). Maybe we can try an Ensemble method such as Gradient-Boosted Decision Trees to see if we can strengthen our model by using an ensemble of weaker trees with weighting to reduce the error in our model.

*Note since this is a complex model, the cell below can take about *16 minutes* or so to run on a small cluster with a couple nodes with about 6GB RAM, go out and grab a coffee and come back :-).*

This GBTRegressor code will be way faster on a larger cluster of course.

A visual explanation of gradient boosted trees:

Let's see what a boosting algorithm, a type of ensemble method, is all about in more detail.

import org.apache.spark.ml.regression.GBTRegressor

val gbt = new GBTRegressor()
gbt.setLabelCol("PE")
gbt.setPredictionCol("Predicted_PE")
gbt.setFeaturesCol("features")
gbt.setSeed(100088121L)
gbt.setMaxBins(100)
gbt.setMaxIter(120)

val gbtPipeline = new Pipeline()
gbtPipeline.setStages(Array(vectorizer, gbt))
//Let's just resuse our CrossValidator

crossval.setEstimator(gbtPipeline)

val paramGrid = new ParamGridBuilder()
  .addGrid(gbt.maxDepth, Array(2, 3))
  .build()
crossval.setEstimatorParamMaps(paramGrid)

//gbt.explainParams
val gbtModel = crossval.fit(trainingSet)
import org.apache.spark.ml.regression.GBTRegressor
gbt: org.apache.spark.ml.regression.GBTRegressor = gbtr_0a275b363831
gbtPipeline: org.apache.spark.ml.Pipeline = pipeline_8b2722444cff
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
	gbtr_0a275b363831-maxDepth: 2
}, {
	gbtr_0a275b363831-maxDepth: 3
})
gbtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
import org.apache.spark.ml.regression.GBTRegressionModel 

val predictionsAndLabels = gbtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)

val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2


println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")  
println (f"R2: $r2")
Root Mean Squared Error: 3.7034735091301307
Explained Variance: 272.208177448154
R2: 0.9520069744321176
import org.apache.spark.ml.regression.GBTRegressionModel
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@79d7946a
rmse: Double = 3.7034735091301307
explainedVariance: Double = 272.208177448154
r2: Double = 0.9520069744321176

We can use the toDebugString method to dump out what our trees and weighting look like:

gbtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[GBTRegressionModel].toDebugString
res116: String =
"GBTRegressionModel: uid=gbtr_0a275b363831, numTrees=120, numFeatures=4
  Tree 0 (weight 1.0):
    If (feature 0 <= 18.595)
     If (feature 0 <= 11.885000000000002)
      If (feature 0 <= 8.575)
       Predict: 483.994943457189
      Else (feature 0 > 8.575)
       Predict: 476.04374262101527
     Else (feature 0 > 11.885000000000002)
      If (feature 0 <= 15.475000000000001)
       Predict: 467.5564649956784
      Else (feature 0 > 15.475000000000001)
       Predict: 459.5010376134889
    Else (feature 0 > 18.595)
     If (feature 1 <= 66.21000000000001)
      If (feature 0 <= 22.055)
       Predict: 452.01076923076914
      Else (feature 0 > 22.055)
       Predict: 443.46937926330156
     Else (feature 1 > 66.21000000000001)
      If (feature 0 <= 25.325)
       Predict: 440.73731707317074
      Else (feature 0 > 25.325)
       Predict: 433.86131215469624
  Tree 1 (weight 0.1):
    If (feature 3 <= 82.55000000000001)
     If (feature 0 <= 26.505000000000003)
      If (feature 3 <= 64.11500000000001)
       Predict: 6.095232469256877
      Else (feature 3 > 64.11500000000001)
       Predict: 1.4337156041793564
     Else (feature 0 > 26.505000000000003)
      If (feature 1 <= 65.55000000000001)
       Predict: -7.6527692217902885
      Else (feature 1 > 65.55000000000001)
       Predict: -0.5844786594493007
    Else (feature 3 > 82.55000000000001)
     If (feature 1 <= 45.754999999999995)
      If (feature 3 <= 93.075)
       Predict: 0.6108402960070604
      Else (feature 3 > 93.075)
       Predict: -3.913147108789527
     Else (feature 1 > 45.754999999999995)
      If (feature 0 <= 18.595)
       Predict: -9.443118256805649
      Else (feature 0 > 18.595)
       Predict: -3.9650066253122347
  Tree 2 (weight 0.1):
    If (feature 1 <= 45.754999999999995)
     If (feature 0 <= 15.475000000000001)
      If (feature 0 <= 14.285)
       Predict: 1.1090321642566017
      Else (feature 0 > 14.285)
       Predict: -3.898383406004269
     Else (feature 0 > 15.475000000000001)
      If (feature 0 <= 18.595)
       Predict: 4.874047316205584
      Else (feature 0 > 18.595)
       Predict: 10.653531335032094
    Else (feature 1 > 45.754999999999995)
     If (feature 0 <= 18.595)
      If (feature 1 <= 58.19)
       Predict: -5.0005796708960615
      Else (feature 1 > 58.19)
       Predict: -13.749053154552547
     Else (feature 0 > 18.595)
      If (feature 2 <= 1009.295)
       Predict: -3.1410617295379555
      Else (feature 2 > 1009.295)
       Predict: 0.8001319329379373
  Tree 3 (weight 0.1):
    If (feature 3 <= 85.52000000000001)
     If (feature 1 <= 55.825)
      If (feature 0 <= 26.505000000000003)
       Predict: 2.695149105254423
      Else (feature 0 > 26.505000000000003)
       Predict: -6.7564705067621675
     Else (feature 1 > 55.825)
      If (feature 1 <= 66.21000000000001)
       Predict: -2.282355645191304
      Else (feature 1 > 66.21000000000001)
       Predict: 0.5508297910243797
    Else (feature 3 > 85.52000000000001)
     If (feature 1 <= 40.629999999999995)
      If (feature 2 <= 1022.8399999999999)
       Predict: 2.236331037122985
      Else (feature 2 > 1022.8399999999999)
       Predict: -7.220151471077029
     Else (feature 1 > 40.629999999999995)
      If (feature 3 <= 89.595)
       Predict: -2.175085332960937
      Else (feature 3 > 89.595)
       Predict: -4.54715329410012
  Tree 4 (weight 0.1):
    If (feature 2 <= 1010.335)
     If (feature 1 <= 43.005)
      If (feature 0 <= 15.475000000000001)
       Predict: -0.004818717215773318
      Else (feature 0 > 15.475000000000001)
       Predict: 5.355639866401019
     Else (feature 1 > 43.005)
      If (feature 1 <= 65.15)
       Predict: -5.132340755716604
      Else (feature 1 > 65.15)
       Predict: -0.7228608840382099
    Else (feature 2 > 1010.335)
     If (feature 3 <= 74.525)
      If (feature 0 <= 29.455)
       Predict: 3.0313483965828176
      Else (feature 0 > 29.455)
       Predict: -2.560863167947768
     Else (feature 3 > 74.525)
      If (feature 1 <= 40.629999999999995)
       Predict: 2.228142113797423
      Else (feature 1 > 40.629999999999995)
       Predict: -1.6052323952407273
  Tree 5 (weight 0.1):
    If (feature 3 <= 81.045)
     If (feature 0 <= 27.585)
      If (feature 3 <= 46.92)
       Predict: 8.005444802948366
      Else (feature 3 > 46.92)
       Predict: 1.301219291279602
     Else (feature 0 > 27.585)
      If (feature 1 <= 65.55000000000001)
       Predict: -6.568175152421089
      Else (feature 1 > 65.55000000000001)
       Predict: -0.8331220915230161
    Else (feature 3 > 81.045)
     If (feature 1 <= 41.519999999999996)
      If (feature 2 <= 1019.835)
       Predict: 1.7054425589091675
      Else (feature 2 > 1019.835)
       Predict: -2.6266728146418195
     Else (feature 1 > 41.519999999999996)
      If (feature 1 <= 41.805)
       Predict: -11.130585327952137
      Else (feature 1 > 41.805)
       Predict: -2.111742422947989
  Tree 6 (weight 0.1):
    If (feature 2 <= 1009.625)
     If (feature 1 <= 43.005)
      If (feature 0 <= 15.475000000000001)
       Predict: -0.4497329015715072
      Else (feature 0 > 15.475000000000001)
       Predict: 3.661440765591046
     Else (feature 1 > 43.005)
      If (feature 1 <= 66.85499999999999)
       Predict: -3.861085082339984
      Else (feature 1 > 66.85499999999999)
       Predict: -0.7492674320362704
    Else (feature 2 > 1009.625)
     If (feature 3 <= 69.32499999999999)
      If (feature 0 <= 29.455)
       Predict: 2.621042446270476
      Else (feature 0 > 29.455)
       Predict: -1.4554021183385886
     Else (feature 3 > 69.32499999999999)
      If (feature 1 <= 58.19)
       Predict: 0.42605173456002876
      Else (feature 1 > 58.19)
       Predict: -1.9554878629891888
  Tree 7 (weight 0.1):
    If (feature 3 <= 86.625)
     If (feature 1 <= 52.795)
      If (feature 0 <= 18.595)
       Predict: 0.4988124937300424
      Else (feature 0 > 18.595)
       Predict: 4.447321094438702
     Else (feature 1 > 52.795)
      If (feature 1 <= 66.21000000000001)
       Predict: -1.721618872277871
      Else (feature 1 > 66.21000000000001)
       Predict: 0.6365209437219329
    Else (feature 3 > 86.625)
     If (feature 0 <= 6.895)
      If (feature 3 <= 87.32499999999999)
       Predict: -10.224737687790185
      Else (feature 3 > 87.32499999999999)
       Predict: 3.712660431036073
     Else (feature 0 > 6.895)
      If (feature 0 <= 8.575)
       Predict: -7.105091794629204
      Else (feature 0 > 8.575)
       Predict: -1.5060749360589718
  Tree 8 (weight 0.1):
    If (feature 2 <= 1008.745)
     If (feature 1 <= 41.665)
      If (feature 0 <= 15.475000000000001)
       Predict: 0.11492519898093705
      Else (feature 0 > 15.475000000000001)
       Predict: 3.532699993937119
     Else (feature 1 > 41.665)
      If (feature 1 <= 66.85499999999999)
       Predict: -3.366654434213031
      Else (feature 1 > 66.85499999999999)
       Predict: -0.9165259817521934
    Else (feature 2 > 1008.745)
     If (feature 3 <= 82.955)
      If (feature 1 <= 51.905)
       Predict: 1.841638760642105
      Else (feature 1 > 51.905)
       Predict: 0.33265178659776373
     Else (feature 3 > 82.955)
      If (feature 3 <= 95.68)
       Predict: -0.4850135884105616
      Else (feature 3 > 95.68)
       Predict: -3.812639024859598
  Tree 9 (weight 0.1):
    If (feature 0 <= 29.455)
     If (feature 3 <= 61.89)
      If (feature 1 <= 71.18)
       Predict: 1.9301726185107941
      Else (feature 1 > 71.18)
       Predict: 8.938327477606107
     Else (feature 3 > 61.89)
      If (feature 0 <= 5.8149999999999995)
       Predict: 4.428862111265314
      Else (feature 0 > 5.8149999999999995)
       Predict: -0.3308347482908286
    Else (feature 0 > 29.455)
     If (feature 1 <= 68.28999999999999)
      If (feature 2 <= 1009.875)
       Predict: -8.941194411728706
      Else (feature 2 > 1009.875)
       Predict: -1.8834474815204216
     Else (feature 1 > 68.28999999999999)
      If (feature 2 <= 1014.405)
       Predict: -0.5760094442636617
      Else (feature 2 > 1014.405)
       Predict: -5.50575933697771
  Tree 10 (weight 0.1):
    If (feature 2 <= 1005.345)
     If (feature 0 <= 27.355)
      If (feature 3 <= 99.3)
       Predict: -0.5544332197918127
      Else (feature 3 > 99.3)
       Predict: -6.049841121821514
     Else (feature 0 > 27.355)
      If (feature 1 <= 70.815)
       Predict: -8.03479585317622
      Else (feature 1 > 70.815)
       Predict: 0.0619269945107018
    Else (feature 2 > 1005.345)
     If (feature 3 <= 72.41499999999999)
      If (feature 0 <= 24.755000000000003)
       Predict: 2.020104454165069
      Else (feature 0 > 24.755000000000003)
       Predict: -0.22964512858748945
     Else (feature 3 > 72.41499999999999)
      If (feature 1 <= 40.7)
       Predict: 1.2987210163143512
      Else (feature 1 > 40.7)
       Predict: -0.8347660227231797
  Tree 11 (weight 0.1):
    If (feature 0 <= 5.0600000000000005)
     If (feature 1 <= 42.3)
      If (feature 1 <= 39.765)
       Predict: 4.261094449524424
      Else (feature 1 > 39.765)
       Predict: 9.140536590115639
     Else (feature 1 > 42.3)
      If (feature 2 <= 1007.835)
       Predict: 0.9746298984436711
      Else (feature 2 > 1007.835)
       Predict: -6.317973546286112
    Else (feature 0 > 5.0600000000000005)
     If (feature 3 <= 95.68)
      If (feature 2 <= 1009.295)
       Predict: -0.8626626541525325
      Else (feature 2 > 1009.295)
       Predict: 0.3906915507169364
     Else (feature 3 > 95.68)
      If (feature 0 <= 11.885000000000002)
       Predict: -6.002679638413293
      Else (feature 0 > 11.885000000000002)
       Predict: -0.7509189525586508
  Tree 12 (weight 0.1):
    If (feature 0 <= 18.595)
     If (feature 1 <= 51.905)
      If (feature 0 <= 13.695)
       Predict: 0.5958576966099496
      Else (feature 0 > 13.695)
       Predict: -1.2694566117501789
     Else (feature 1 > 51.905)
      If (feature 1 <= 58.19)
       Predict: -4.248373100253223
      Else (feature 1 > 58.19)
       Predict: -9.144729000689791
    Else (feature 0 > 18.595)
     If (feature 1 <= 47.64)
      If (feature 2 <= 1012.675)
       Predict: 0.8560563666774771
      Else (feature 2 > 1012.675)
       Predict: 10.801233083620462
     Else (feature 1 > 47.64)
      If (feature 0 <= 20.015)
       Predict: 3.1978561830060213
      Else (feature 0 > 20.015)
       Predict: -0.16716555702980626
  Tree 13 (weight 0.1):
    If (feature 0 <= 28.655)
     If (feature 1 <= 66.21000000000001)
      If (feature 1 <= 58.19)
       Predict: 0.34765099851241454
      Else (feature 1 > 58.19)
       Predict: -1.8189329025195076
     Else (feature 1 > 66.21000000000001)
      If (feature 0 <= 22.525)
       Predict: 6.490484815153365
      Else (feature 0 > 22.525)
       Predict: 0.7933381751314565
    Else (feature 0 > 28.655)
     If (feature 1 <= 68.28999999999999)
      If (feature 2 <= 1005.665)
       Predict: -8.922459399148678
      Else (feature 2 > 1005.665)
       Predict: -2.3989441108132668
     Else (feature 1 > 68.28999999999999)
      If (feature 2 <= 1018.215)
       Predict: -0.3023338438804105
      Else (feature 2 > 1018.215)
       Predict: 14.559949112833806
  Tree 14 (weight 0.1):
    If (feature 0 <= 15.475000000000001)
     If (feature 0 <= 13.695)
      If (feature 0 <= 11.885000000000002)
       Predict: -0.8897676968273979
      Else (feature 0 > 11.885000000000002)
       Predict: 3.5874467728768487
     Else (feature 0 > 13.695)
      If (feature 1 <= 46.345)
       Predict: -3.2690852418064273
      Else (feature 1 > 46.345)
       Predict: -8.844433418899179
    Else (feature 0 > 15.475000000000001)
     If (feature 1 <= 43.175)
      If (feature 1 <= 41.805)
       Predict: 3.298855216614116
      Else (feature 1 > 41.805)
       Predict: 9.150659154207167
     Else (feature 1 > 43.175)
      If (feature 2 <= 1012.495)
       Predict: -0.7109832273625656
      Else (feature 2 > 1012.495)
       Predict: 1.1631179843236674
  Tree 15 (weight 0.1):
    If (feature 3 <= 90.055)
     If (feature 0 <= 30.41)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.01732155577258642
      Else (feature 1 > 66.21000000000001)
       Predict: 1.3432966941310502
     Else (feature 0 > 30.41)
      If (feature 1 <= 69.465)
       Predict: -3.6657811330207344
      Else (feature 1 > 69.465)
       Predict: -0.38803561342243853
    Else (feature 3 > 90.055)
     If (feature 2 <= 1015.725)
      If (feature 1 <= 47.64)
       Predict: 1.5063080039677825
      Else (feature 1 > 47.64)
       Predict: -1.860635777865782
     Else (feature 2 > 1015.725)
      If (feature 1 <= 40.7)
       Predict: 0.21921885078759318
      Else (feature 1 > 40.7)
       Predict: -4.793242614118129
  Tree 16 (weight 0.1):
    If (feature 0 <= 6.895)
     If (feature 3 <= 67.42500000000001)
      If (feature 1 <= 39.620000000000005)
       Predict: -1.8477082180199003
      Else (feature 1 > 39.620000000000005)
       Predict: 16.387935166882745
     Else (feature 3 > 67.42500000000001)
      If (feature 0 <= 5.0600000000000005)
       Predict: 4.152127693563297
      Else (feature 0 > 5.0600000000000005)
       Predict: 0.7556214745509566
    Else (feature 0 > 6.895)
     If (feature 0 <= 8.575)
      If (feature 2 <= 1022.8399999999999)
       Predict: -2.9436476590152036
      Else (feature 2 > 1022.8399999999999)
       Predict: -8.032234733606465
     Else (feature 0 > 8.575)
      If (feature 0 <= 9.325)
       Predict: 5.082738415532321
      Else (feature 0 > 9.325)
       Predict: -0.032287806549855816
  Tree 17 (weight 0.1):
    If (feature 3 <= 61.89)
     If (feature 0 <= 28.655)
      If (feature 0 <= 11.885000000000002)
       Predict: -4.455566502931916
      Else (feature 0 > 11.885000000000002)
       Predict: 1.9027943448616742
     Else (feature 0 > 28.655)
      If (feature 1 <= 66.21000000000001)
       Predict: -3.1810487244675034
      Else (feature 1 > 66.21000000000001)
       Predict: 0.11525363261816625
    Else (feature 3 > 61.89)
     If (feature 1 <= 43.175)
      If (feature 0 <= 18.595)
       Predict: 0.22262511130066664
      Else (feature 0 > 18.595)
       Predict: 10.13639446335034
     Else (feature 1 > 43.175)
      If (feature 0 <= 22.055)
       Predict: -1.4775024201129299
      Else (feature 0 > 22.055)
       Predict: 0.20591189539330298
  Tree 18 (weight 0.1):
    If (feature 2 <= 1005.345)
     If (feature 1 <= 70.815)
      If (feature 0 <= 23.564999999999998)
       Predict: -0.178777362918765
      Else (feature 0 > 23.564999999999998)
       Predict: -4.52113806132068
     Else (feature 1 > 70.815)
      If (feature 3 <= 60.025000000000006)
       Predict: 4.247605743793766
      Else (feature 3 > 60.025000000000006)
       Predict: -0.26865379510738685
    Else (feature 2 > 1005.345)
     If (feature 1 <= 66.21000000000001)
      If (feature 1 <= 55.825)
       Predict: 0.3773109266288433
      Else (feature 1 > 55.825)
       Predict: -1.3659380946007862
     Else (feature 1 > 66.21000000000001)
      If (feature 0 <= 21.335)
       Predict: 8.448170645066464
      Else (feature 0 > 21.335)
       Predict: 0.5048679905700459
  Tree 19 (weight 0.1):
    If (feature 0 <= 18.595)
     If (feature 1 <= 51.905)
      If (feature 0 <= 17.655)
       Predict: -0.003054757854134698
      Else (feature 0 > 17.655)
       Predict: -2.9290357116654935
     Else (feature 1 > 51.905)
      If (feature 1 <= 66.525)
       Predict: -4.061825439604536
      Else (feature 1 > 66.525)
       Predict: -11.67844591879286
    Else (feature 0 > 18.595)
     If (feature 0 <= 23.945)
      If (feature 3 <= 74.525)
       Predict: 3.807909998319029
      Else (feature 3 > 74.525)
       Predict: 0.008459259251505081
     Else (feature 0 > 23.945)
      If (feature 1 <= 74.915)
       Predict: -0.6429811796826012
      Else (feature 1 > 74.915)
       Predict: 2.099670946504916
  Tree 20 (weight 0.1):
    If (feature 0 <= 15.475000000000001)
     If (feature 0 <= 14.285)
      If (feature 0 <= 11.885000000000002)
       Predict: -0.7234702732306101
      Else (feature 0 > 11.885000000000002)
       Predict: 1.589299673192479
     Else (feature 0 > 14.285)
      If (feature 2 <= 1016.495)
       Predict: -2.4381805412578545
      Else (feature 2 > 1016.495)
       Predict: -6.544687605774689
    Else (feature 0 > 15.475000000000001)
     If (feature 1 <= 43.005)
      If (feature 2 <= 1008.405)
       Predict: 0.8119248626873221
      Else (feature 2 > 1008.405)
       Predict: 5.506769369239865
     Else (feature 1 > 43.005)
      If (feature 2 <= 1012.935)
       Predict: -0.5028175384892806
      Else (feature 2 > 1012.935)
       Predict: 1.0290531238165197
  Tree 21 (weight 0.1):
    If (feature 0 <= 6.895)
     If (feature 3 <= 67.42500000000001)
      If (feature 1 <= 39.620000000000005)
       Predict: -0.9752219334588972
      Else (feature 1 > 39.620000000000005)
       Predict: 13.369575512848845
     Else (feature 3 > 67.42500000000001)
      If (feature 1 <= 42.705)
       Predict: 1.850492181024754
      Else (feature 1 > 42.705)
       Predict: -2.033710059657357
    Else (feature 0 > 6.895)
     If (feature 0 <= 8.575)
      If (feature 2 <= 1022.8399999999999)
       Predict: -2.256120115332435
      Else (feature 2 > 1022.8399999999999)
       Predict: -6.380948299395909
     Else (feature 0 > 8.575)
      If (feature 0 <= 9.594999999999999)
       Predict: 3.4448565562285904
      Else (feature 0 > 9.594999999999999)
       Predict: -0.05858832726578875
  Tree 22 (weight 0.1):
    If (feature 0 <= 22.055)
     If (feature 0 <= 21.095)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.17987193554240402
      Else (feature 1 > 66.21000000000001)
       Predict: 6.044335675519052
     Else (feature 0 > 21.095)
      If (feature 1 <= 66.21000000000001)
       Predict: -5.809637597188877
      Else (feature 1 > 66.21000000000001)
       Predict: 2.7761443044302214
    Else (feature 0 > 22.055)
     If (feature 0 <= 23.564999999999998)
      If (feature 1 <= 64.74000000000001)
       Predict: 5.951769778696803
      Else (feature 1 > 64.74000000000001)
       Predict: -0.30197045172071896
     Else (feature 0 > 23.564999999999998)
      If (feature 1 <= 44.519999999999996)
       Predict: -7.283292958317056
      Else (feature 1 > 44.519999999999996)
       Predict: -0.19387573131258415
  Tree 23 (weight 0.1):
    If (feature 3 <= 53.025000000000006)
     If (feature 0 <= 24.945)
      If (feature 0 <= 18.29)
       Predict: 1.0381040338364682
      Else (feature 0 > 18.29)
       Predict: 5.164469183375362
     Else (feature 0 > 24.945)
      If (feature 1 <= 66.21000000000001)
       Predict: -1.0707595335847206
      Else (feature 1 > 66.21000000000001)
       Predict: 0.8108674466901084
    Else (feature 3 > 53.025000000000006)
     If (feature 1 <= 73.725)
      If (feature 1 <= 71.505)
       Predict: -0.0525992725981239
      Else (feature 1 > 71.505)
       Predict: -2.807221561152817
     Else (feature 1 > 73.725)
      If (feature 2 <= 1017.295)
       Predict: 1.037788102485777
      Else (feature 2 > 1017.295)
       Predict: 7.287181806639116
  Tree 24 (weight 0.1):
    If (feature 2 <= 1004.505)
     If (feature 1 <= 70.815)
      If (feature 1 <= 59.144999999999996)
       Predict: -0.3743851611431973
      Else (feature 1 > 59.144999999999996)
       Predict: -4.648812647772385
     Else (feature 1 > 70.815)
      If (feature 0 <= 27.884999999999998)
       Predict: 2.4545579195950995
      Else (feature 0 > 27.884999999999998)
       Predict: -0.32777974793969294
    Else (feature 2 > 1004.505)
     If (feature 0 <= 22.055)
      If (feature 0 <= 20.795)
       Predict: 0.045218490740101897
      Else (feature 0 > 20.795)
       Predict: -3.3620385127109333
     Else (feature 0 > 22.055)
      If (feature 0 <= 22.955)
       Predict: 4.485324626081587
      Else (feature 0 > 22.955)
       Predict: 0.13166549369330485
  Tree 25 (weight 0.1):
    If (feature 0 <= 18.595)
     If (feature 1 <= 58.19)
      If (feature 0 <= 17.655)
       Predict: -0.023636417468335308
      Else (feature 0 > 17.655)
       Predict: -2.607492744918568
     Else (feature 1 > 58.19)
      If (feature 1 <= 66.525)
       Predict: -4.8994505786627665
      Else (feature 1 > 66.525)
       Predict: -10.07085237281708
    Else (feature 0 > 18.595)
     If (feature 0 <= 19.725)
      If (feature 1 <= 66.21000000000001)
       Predict: 2.2716539497960406
      Else (feature 1 > 66.21000000000001)
       Predict: 13.879740983230867
     Else (feature 0 > 19.725)
      If (feature 1 <= 43.005)
       Predict: 5.616411926544602
      Else (feature 1 > 43.005)
       Predict: -0.00532316539213451
  Tree 26 (weight 0.1):
    If (feature 3 <= 93.075)
     If (feature 1 <= 55.825)
      If (feature 0 <= 22.055)
       Predict: 0.15441544139943786
      Else (feature 0 > 22.055)
       Predict: 2.7626508552722306
     Else (feature 1 > 55.825)
      If (feature 1 <= 66.21000000000001)
       Predict: -1.2060290652635515
      Else (feature 1 > 66.21000000000001)
       Predict: 0.4827535356971057
    Else (feature 3 > 93.075)
     If (feature 2 <= 1015.725)
      If (feature 0 <= 7.34)
       Predict: 4.241421869391143
      Else (feature 0 > 7.34)
       Predict: -0.704916847045625
     Else (feature 2 > 1015.725)
      If (feature 0 <= 11.885000000000002)
       Predict: -5.4904249010577795
      Else (feature 0 > 11.885000000000002)
       Predict: 0.0976872424106726
  Tree 27 (weight 0.1):
    If (feature 2 <= 1008.745)
     If (feature 1 <= 41.665)
      If (feature 1 <= 39.765)
       Predict: -1.0511879426704696
      Else (feature 1 > 39.765)
       Predict: 1.894883627758776
     Else (feature 1 > 41.665)
      If (feature 1 <= 41.805)
       Predict: -13.576686832482961
      Else (feature 1 > 41.805)
       Predict: -0.7168167899342832
    Else (feature 2 > 1008.745)
     If (feature 0 <= 15.475000000000001)
      If (feature 0 <= 13.695)
       Predict: 0.303140697564446
      Else (feature 0 > 13.695)
       Predict: -2.8034862119109945
     Else (feature 0 > 15.475000000000001)
      If (feature 1 <= 55.825)
       Predict: 2.0736158373993576
      Else (feature 1 > 55.825)
       Predict: -0.03336709818500243
  Tree 28 (weight 0.1):
    If (feature 2 <= 1001.785)
     If (feature 3 <= 69.86500000000001)
      If (feature 1 <= 69.465)
       Predict: -7.299995136853619
      Else (feature 1 > 69.465)
       Predict: 0.23757420536270835
     Else (feature 3 > 69.86500000000001)
      If (feature 0 <= 7.34)
       Predict: 3.4313360513286284
      Else (feature 0 > 7.34)
       Predict: -1.276734468456009
    Else (feature 2 > 1001.785)
     If (feature 0 <= 11.885000000000002)
      If (feature 0 <= 11.335)
       Predict: 0.04499264982858887
      Else (feature 0 > 11.335)
       Predict: -6.156735713959919
     Else (feature 0 > 11.885000000000002)
      If (feature 0 <= 12.715)
       Predict: 4.4864210547378915
      Else (feature 0 > 12.715)
       Predict: 0.030721690290287165
  Tree 29 (weight 0.1):
    If (feature 1 <= 66.21000000000001)
     If (feature 0 <= 26.505000000000003)
      If (feature 0 <= 22.055)
       Predict: -0.3694472776628706
      Else (feature 0 > 22.055)
       Predict: 1.5208925945902059
     Else (feature 0 > 26.505000000000003)
      If (feature 1 <= 43.510000000000005)
       Predict: -17.99975152056241
      Else (feature 1 > 43.510000000000005)
       Predict: -2.4033598663496183
    Else (feature 1 > 66.21000000000001)
     If (feature 0 <= 22.055)
      If (feature 0 <= 18.595)
       Predict: -7.600012529438
      Else (feature 0 > 18.595)
       Predict: 6.469471961408998
     Else (feature 0 > 22.055)
      If (feature 0 <= 25.325)
       Predict: -2.6540186758683166
      Else (feature 0 > 25.325)
       Predict: 0.9869775581610103
  Tree 30 (weight 0.1):
    If (feature 0 <= 5.0600000000000005)
     If (feature 1 <= 42.3)
      If (feature 3 <= 95.68)
       Predict: 2.6307256050737506
      Else (feature 3 > 95.68)
       Predict: 7.168878406232186
     Else (feature 1 > 42.3)
      If (feature 2 <= 1007.835)
       Predict: -0.1884983669158752
      Else (feature 2 > 1007.835)
       Predict: -5.920088632100639
    Else (feature 0 > 5.0600000000000005)
     If (feature 1 <= 37.815)
      If (feature 0 <= 11.885000000000002)
       Predict: -3.8096010917307517
      Else (feature 0 > 11.885000000000002)
       Predict: 3.5943708074284917
     Else (feature 1 > 37.815)
      If (feature 1 <= 41.519999999999996)
       Predict: 0.6752927073561888
      Else (feature 1 > 41.519999999999996)
       Predict: -0.14342050966250913
  Tree 31 (weight 0.1):
    If (feature 3 <= 99.3)
     If (feature 0 <= 31.155)
      If (feature 3 <= 46.92)
       Predict: 2.011051499336945
      Else (feature 3 > 46.92)
       Predict: 0.012791339921714258
     Else (feature 0 > 31.155)
      If (feature 2 <= 1014.405)
       Predict: -0.916397796921109
      Else (feature 2 > 1014.405)
       Predict: -6.832504867736499
    Else (feature 3 > 99.3)
     If (feature 1 <= 39.765)
      If (feature 1 <= 38.41)
       Predict: -5.505405887296888
      Else (feature 1 > 38.41)
       Predict: -16.748187635942333
     Else (feature 1 > 39.765)
      If (feature 0 <= 16.04)
       Predict: 0.7708563952983728
      Else (feature 0 > 16.04)
       Predict: -3.909609799859729
  Tree 32 (weight 0.1):
    If (feature 1 <= 66.21000000000001)
     If (feature 1 <= 64.74000000000001)
      If (feature 2 <= 1011.515)
       Predict: -1.0002623040885374
      Else (feature 2 > 1011.515)
       Predict: 0.3591184679910596
     Else (feature 1 > 64.74000000000001)
      If (feature 2 <= 1006.775)
       Predict: -13.788703659238209
      Else (feature 2 > 1006.775)
       Predict: -2.4620285364725656
    Else (feature 1 > 66.21000000000001)
     If (feature 1 <= 69.09)
      If (feature 0 <= 22.525)
       Predict: 6.088217885246919
      Else (feature 0 > 22.525)
       Predict: 1.0364537580784474
     Else (feature 1 > 69.09)
      If (feature 0 <= 25.325)
       Predict: -2.7946704533493856
      Else (feature 0 > 25.325)
       Predict: 0.6607665941219878
  Tree 33 (weight 0.1):
    If (feature 2 <= 1028.14)
     If (feature 0 <= 29.455)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.08158010506593574
      Else (feature 1 > 66.21000000000001)
       Predict: 0.8815313937000332
     Else (feature 0 > 29.455)
      If (feature 1 <= 44.519999999999996)
       Predict: -12.581939252812768
      Else (feature 1 > 44.519999999999996)
       Predict: -0.77008278158028
    Else (feature 2 > 1028.14)
     If (feature 0 <= 8.575)
      If (feature 1 <= 39.45)
       Predict: 0.6766771966451415
      Else (feature 1 > 39.45)
       Predict: -8.445008186341592
     Else (feature 0 > 8.575)
      If (feature 0 <= 8.96)
       Predict: 3.777292703648982
      Else (feature 0 > 8.96)
       Predict: -2.398045803854934
  Tree 34 (weight 0.1):
    If (feature 0 <= 6.895)
     If (feature 3 <= 67.42500000000001)
      If (feature 1 <= 39.620000000000005)
       Predict: -0.6804590872791323
      Else (feature 1 > 39.620000000000005)
       Predict: 10.518093452986212
     Else (feature 3 > 67.42500000000001)
      If (feature 1 <= 42.705)
       Predict: 1.3098801739379287
      Else (feature 1 > 42.705)
       Predict: -1.7450883352732074
    Else (feature 0 > 6.895)
     If (feature 0 <= 8.575)
      If (feature 0 <= 7.765)
       Predict: -0.7816329288490581
      Else (feature 0 > 7.765)
       Predict: -3.7319393815256547
     Else (feature 0 > 8.575)
      If (feature 0 <= 9.965)
       Predict: 2.5600814387681337
      Else (feature 0 > 9.965)
       Predict: -0.06944896856946733
  Tree 35 (weight 0.1):
    If (feature 3 <= 61.89)
     If (feature 1 <= 39.34)
      If (feature 0 <= 16.695)
       Predict: -4.078381955056753
      Else (feature 0 > 16.695)
       Predict: -16.616821684050763
     Else (feature 1 > 39.34)
      If (feature 1 <= 40.82)
       Predict: 4.4394084324272045
      Else (feature 1 > 40.82)
       Predict: 0.4663514281701948
    Else (feature 3 > 61.89)
     If (feature 1 <= 58.19)
      If (feature 0 <= 22.055)
       Predict: -0.027831559557693095
      Else (feature 0 > 22.055)
       Predict: 2.492136574233702
     Else (feature 1 > 58.19)
      If (feature 0 <= 18.595)
       Predict: -4.183073089901298
      Else (feature 0 > 18.595)
       Predict: -0.40866909936948326
  Tree 36 (weight 0.1):
    If (feature 2 <= 1004.505)
     If (feature 1 <= 38.754999999999995)
      If (feature 3 <= 77.195)
       Predict: 9.579430817769946
      Else (feature 3 > 77.195)
       Predict: 2.6305173165509195
     Else (feature 1 > 38.754999999999995)
      If (feature 1 <= 70.065)
       Predict: -1.801035005150415
      Else (feature 1 > 70.065)
       Predict: 0.4054557218074593
    Else (feature 2 > 1004.505)
     If (feature 2 <= 1017.475)
      If (feature 1 <= 43.175)
       Predict: 1.09341093192285
      Else (feature 1 > 43.175)
       Predict: -0.03229585395374685
     Else (feature 2 > 1017.475)
      If (feature 2 <= 1019.365)
       Predict: -1.7719467091167656
      Else (feature 2 > 1019.365)
       Predict: 0.2537098831339789
  Tree 37 (weight 0.1):
    If (feature 0 <= 15.475000000000001)
     If (feature 0 <= 13.695)
      If (feature 0 <= 11.885000000000002)
       Predict: -0.5104239863875834
      Else (feature 0 > 11.885000000000002)
       Predict: 2.1202163252308432
     Else (feature 0 > 13.695)
      If (feature 3 <= 78.38499999999999)
       Predict: -3.379040627299855
      Else (feature 3 > 78.38499999999999)
       Predict: -0.9911440360990582
    Else (feature 0 > 15.475000000000001)
     If (feature 0 <= 16.395)
      If (feature 3 <= 67.42500000000001)
       Predict: 0.4887402058080506
      Else (feature 3 > 67.42500000000001)
       Predict: 3.884289185845989
     Else (feature 0 > 16.395)
      If (feature 2 <= 1020.565)
       Predict: -0.02862374251288089
      Else (feature 2 > 1020.565)
       Predict: 3.15734851002617
  Tree 38 (weight 0.1):
    If (feature 3 <= 43.385)
     If (feature 0 <= 24.945)
      If (feature 2 <= 1014.405)
       Predict: 1.603183026814036
      Else (feature 2 > 1014.405)
       Predict: 7.05673098720603
     Else (feature 0 > 24.945)
      If (feature 0 <= 26.295)
       Predict: -5.886157652325024
      Else (feature 0 > 26.295)
       Predict: 0.7387886738447553
    Else (feature 3 > 43.385)
     If (feature 1 <= 73.725)
      If (feature 1 <= 71.505)
       Predict: 0.008754170391068655
      Else (feature 1 > 71.505)
       Predict: -2.0178119354056765
     Else (feature 1 > 73.725)
      If (feature 1 <= 77.42)
       Predict: 1.8396223170900134
      Else (feature 1 > 77.42)
       Predict: -1.5747478023010713
  Tree 39 (weight 0.1):
    If (feature 0 <= 22.055)
     If (feature 0 <= 20.795)
      If (feature 0 <= 18.595)
       Predict: -0.25201916420658693
      Else (feature 0 > 18.595)
       Predict: 1.5718496558845116
     Else (feature 0 > 20.795)
      If (feature 1 <= 66.21000000000001)
       Predict: -3.7201562729508804
      Else (feature 1 > 66.21000000000001)
       Predict: 2.087916157719636
    Else (feature 0 > 22.055)
     If (feature 0 <= 23.564999999999998)
      If (feature 2 <= 1012.015)
       Predict: 0.7371264627237751
      Else (feature 2 > 1012.015)
       Predict: 4.6381746481309065
     Else (feature 0 > 23.564999999999998)
      If (feature 1 <= 44.519999999999996)
       Predict: -5.772062593218147
      Else (feature 1 > 44.519999999999996)
       Predict: -0.12977023255758624
  Tree 40 (weight 0.1):
    If (feature 0 <= 5.0600000000000005)
     If (feature 3 <= 95.68)
      If (feature 2 <= 1011.105)
       Predict: -3.365769010003795
      Else (feature 2 > 1011.105)
       Predict: 2.955050770650336
     Else (feature 3 > 95.68)
      If (feature 2 <= 1008.205)
       Predict: 2.728200788704399
      Else (feature 2 > 1008.205)
       Predict: 6.919382935391042
    Else (feature 0 > 5.0600000000000005)
     If (feature 3 <= 96.61)
      If (feature 1 <= 37.815)
       Predict: -1.3949762323292278
      Else (feature 1 > 37.815)
       Predict: 0.06411431759031856
     Else (feature 3 > 96.61)
      If (feature 0 <= 11.605)
       Predict: -3.491961189446582
      Else (feature 0 > 11.605)
       Predict: -0.16406940197018208
  Tree 41 (weight 0.1):
    If (feature 0 <= 30.41)
     If (feature 1 <= 66.21000000000001)
      If (feature 1 <= 58.85)
       Predict: 0.15540001609212736
      Else (feature 1 > 58.85)
       Predict: -1.1352706510871309
     Else (feature 1 > 66.21000000000001)
      If (feature 0 <= 25.325)
       Predict: -0.6390388689881783
      Else (feature 0 > 25.325)
       Predict: 1.3909471658872326
    Else (feature 0 > 30.41)
     If (feature 2 <= 1014.405)
      If (feature 1 <= 56.894999999999996)
       Predict: -9.841212730443857
      Else (feature 1 > 56.894999999999996)
       Predict: -0.5054201979116707
     Else (feature 2 > 1014.405)
      If (feature 1 <= 74.915)
       Predict: -5.898616989217175
      Else (feature 1 > 74.915)
       Predict: 1.042314191558674
  Tree 42 (weight 0.1):
    If (feature 2 <= 1009.625)
     If (feature 1 <= 43.005)
      If (feature 1 <= 42.025000000000006)
       Predict: -0.05077288158998095
      Else (feature 1 > 42.025000000000006)
       Predict: 3.619244461816007
     Else (feature 1 > 43.005)
      If (feature 0 <= 18.595)
       Predict: -3.5057172058309307
      Else (feature 0 > 18.595)
       Predict: -0.32537979322253646
    Else (feature 2 > 1009.625)
     If (feature 2 <= 1017.475)
      If (feature 1 <= 55.825)
       Predict: 0.8076710222538108
      Else (feature 1 > 55.825)
       Predict: -0.01784249647067053
     Else (feature 2 > 1017.475)
      If (feature 2 <= 1019.365)
       Predict: -1.4312272708033218
      Else (feature 2 > 1019.365)
       Predict: 0.20844195286029432
  Tree 43 (weight 0.1):
    If (feature 0 <= 18.595)
     If (feature 0 <= 17.655)
      If (feature 0 <= 15.475000000000001)
       Predict: -0.2913115404147692
      Else (feature 0 > 15.475000000000001)
       Predict: 1.1145997107947745
     Else (feature 0 > 17.655)
      If (feature 1 <= 37.815)
       Predict: 14.538914074443028
      Else (feature 1 > 37.815)
       Predict: -3.0407663665320683
    Else (feature 0 > 18.595)
     If (feature 2 <= 1020.325)
      If (feature 1 <= 43.175)
       Predict: 3.881117973012625
      Else (feature 1 > 43.175)
       Predict: 0.04538590552170286
     Else (feature 2 > 1020.325)
      If (feature 1 <= 64.74000000000001)
       Predict: 4.662857871344832
      Else (feature 1 > 64.74000000000001)
       Predict: -43.5190245896606
  Tree 44 (weight 0.1):
    If (feature 0 <= 6.895)
     If (feature 3 <= 67.42500000000001)
      If (feature 1 <= 39.620000000000005)
       Predict: -0.7153417257252007
      Else (feature 1 > 39.620000000000005)
       Predict: 8.417772324738223
     Else (feature 3 > 67.42500000000001)
      If (feature 2 <= 1010.985)
       Predict: 2.5111312207852263
      Else (feature 2 > 1010.985)
       Predict: 0.29950092637128345
    Else (feature 0 > 6.895)
     If (feature 0 <= 8.575)
      If (feature 1 <= 40.06)
       Predict: -3.30672576579827
      Else (feature 1 > 40.06)
       Predict: -0.45097750406116727
     Else (feature 0 > 8.575)
      If (feature 0 <= 9.325)
       Predict: 3.226884779601992
      Else (feature 0 > 9.325)
       Predict: -0.041218673012550146
  Tree 45 (weight 0.1):
    If (feature 2 <= 1028.14)
     If (feature 0 <= 10.395)
      If (feature 3 <= 92.22999999999999)
       Predict: 1.0355113706204075
      Else (feature 3 > 92.22999999999999)
       Predict: -1.4603765116034264
     Else (feature 0 > 10.395)
      If (feature 0 <= 11.885000000000002)
       Predict: -2.630098257038007
      Else (feature 0 > 11.885000000000002)
       Predict: 0.09582680902226459
    Else (feature 2 > 1028.14)
     If (feature 0 <= 8.575)
      If (feature 1 <= 39.45)
       Predict: 1.107359449029309
      Else (feature 1 > 39.45)
       Predict: -6.096185488604492
     Else (feature 0 > 8.575)
      If (feature 3 <= 70.57499999999999)
       Predict: -3.516006231223605
      Else (feature 3 > 70.57499999999999)
       Predict: -0.26144006873351155
  Tree 46 (weight 0.1):
    If (feature 0 <= 27.884999999999998)
     If (feature 1 <= 66.21000000000001)
      If (feature 0 <= 25.145)
       Predict: 0.06412914679730906
      Else (feature 0 > 25.145)
       Predict: -1.554041618425865
     Else (feature 1 > 66.21000000000001)
      If (feature 0 <= 25.325)
       Predict: -0.4748636523867588
      Else (feature 0 > 25.325)
       Predict: 2.4595627925276826
    Else (feature 0 > 27.884999999999998)
     If (feature 3 <= 61.89)
      If (feature 1 <= 71.895)
       Predict: -0.4429995884239883
      Else (feature 1 > 71.895)
       Predict: 1.4321627506429122
     Else (feature 3 > 61.89)
      If (feature 1 <= 71.505)
       Predict: -0.2617373838209719
      Else (feature 1 > 71.505)
       Predict: -2.637373068367584
  Tree 47 (weight 0.1):
    If (feature 3 <= 61.89)
     If (feature 1 <= 39.34)
      If (feature 0 <= 16.695)
       Predict: -2.855821258258964
      Else (feature 0 > 16.695)
       Predict: -13.80709248590955
     Else (feature 1 > 39.34)
      If (feature 1 <= 74.915)
       Predict: 0.35443616318343385
      Else (feature 1 > 74.915)
       Predict: 2.9400682899293233
    Else (feature 3 > 61.89)
     If (feature 1 <= 71.505)
      If (feature 1 <= 66.85499999999999)
       Predict: -0.142107366817925
      Else (feature 1 > 66.85499999999999)
       Predict: 1.0352328327059535
     Else (feature 1 > 71.505)
      If (feature 1 <= 73.00999999999999)
       Predict: -2.947898208728739
      Else (feature 1 > 73.00999999999999)
       Predict: -0.0830706492691125
  Tree 48 (weight 0.1):
    If (feature 2 <= 1004.505)
     If (feature 1 <= 38.754999999999995)
      If (feature 3 <= 86.625)
       Predict: 4.921331281961159
      Else (feature 3 > 86.625)
       Predict: -5.961237317667383
     Else (feature 1 > 38.754999999999995)
      If (feature 1 <= 39.765)
       Predict: -4.436072018762161
      Else (feature 1 > 39.765)
       Predict: -0.7283906418193187
    Else (feature 2 > 1004.505)
     If (feature 0 <= 22.055)
      If (feature 0 <= 20.795)
       Predict: 0.044899959208437666
      Else (feature 0 > 20.795)
       Predict: -2.2295677836603995
     Else (feature 0 > 22.055)
      If (feature 0 <= 23.564999999999998)
       Predict: 2.3559950404053414
      Else (feature 0 > 23.564999999999998)
       Predict: -0.06950420285239005
  Tree 49 (weight 0.1):
    If (feature 1 <= 41.435)
     If (feature 0 <= 11.885000000000002)
      If (feature 0 <= 11.335)
       Predict: -0.02697483446966382
      Else (feature 0 > 11.335)
       Predict: -3.7031660865730367
     Else (feature 0 > 11.885000000000002)
      If (feature 0 <= 13.265)
       Predict: 4.209724879257512
      Else (feature 0 > 13.265)
       Predict: 0.41602586770224464
    Else (feature 1 > 41.435)
     If (feature 1 <= 41.805)
      If (feature 3 <= 89.595)
       Predict: -1.6099299736449546
      Else (feature 3 > 89.595)
       Predict: -9.419799062932288
     Else (feature 1 > 41.805)
      If (feature 1 <= 43.175)
       Predict: 1.5951066240527068
      Else (feature 1 > 43.175)
       Predict: -0.11481901338423915
  Tree 50 (weight 0.1):
    If (feature 1 <= 38.41)
     If (feature 0 <= 11.885000000000002)
      If (feature 2 <= 1016.665)
       Predict: -5.344526613859499
      Else (feature 2 > 1016.665)
       Predict: 0.058134490545794976
     Else (feature 0 > 11.885000000000002)
      If (feature 0 <= 16.985)
       Predict: 1.537246393097642
      Else (feature 0 > 16.985)
       Predict: 9.920778277565365
    Else (feature 1 > 38.41)
     If (feature 0 <= 13.485)
      If (feature 1 <= 51.045)
       Predict: 0.6675749827847329
      Else (feature 1 > 51.045)
       Predict: -5.737026170963195
     Else (feature 0 > 13.485)
      If (feature 0 <= 15.475000000000001)
       Predict: -1.7210713764803844
      Else (feature 0 > 15.475000000000001)
       Predict: 0.09027853735147576
  Tree 51 (weight 0.1):
    If (feature 0 <= 31.155)
     If (feature 3 <= 48.230000000000004)
      If (feature 2 <= 1020.325)
       Predict: 0.9307527734280435
      Else (feature 2 > 1020.325)
       Predict: 9.070547343579028
     Else (feature 3 > 48.230000000000004)
      If (feature 1 <= 73.725)
       Predict: -0.06359121142988887
      Else (feature 1 > 73.725)
       Predict: 1.049949579330099
    Else (feature 0 > 31.155)
     If (feature 1 <= 63.08)
      If (feature 1 <= 44.519999999999996)
       Predict: -6.355864033256125
      Else (feature 1 > 44.519999999999996)
       Predict: 13.6760008529786
     Else (feature 1 > 63.08)
      If (feature 1 <= 68.28999999999999)
       Predict: -3.953494984806905
      Else (feature 1 > 68.28999999999999)
       Predict: -0.6145841300086397
  Tree 52 (weight 0.1):
    If (feature 2 <= 1012.495)
     If (feature 1 <= 41.435)
      If (feature 1 <= 39.975)
       Predict: -0.4047468839922722
      Else (feature 1 > 39.975)
       Predict: 2.509624688414308
     Else (feature 1 > 41.435)
      If (feature 1 <= 41.805)
       Predict: -6.811044477667833
      Else (feature 1 > 41.805)
       Predict: -0.26889174065489546
    Else (feature 2 > 1012.495)
     If (feature 3 <= 92.22999999999999)
      If (feature 1 <= 58.19)
       Predict: 0.7048711166950787
      Else (feature 1 > 58.19)
       Predict: -0.5475390646726656
     Else (feature 3 > 92.22999999999999)
      If (feature 1 <= 41.805)
       Predict: -3.7013459723577355
      Else (feature 1 > 41.805)
       Predict: 0.7237930378019226
  Tree 53 (weight 0.1):
    If (feature 0 <= 18.595)
     If (feature 0 <= 17.335)
      If (feature 3 <= 74.08500000000001)
       Predict: -0.8631764946312587
      Else (feature 3 > 74.08500000000001)
       Predict: 0.2803856631344212
     Else (feature 0 > 17.335)
      If (feature 1 <= 42.705)
       Predict: 0.9335711174385192
      Else (feature 1 > 42.705)
       Predict: -2.950020164379197
    Else (feature 0 > 18.595)
     If (feature 2 <= 1013.395)
      If (feature 1 <= 66.85499999999999)
       Predict: -0.7231135633124072
      Else (feature 1 > 66.85499999999999)
       Predict: 0.41724670068145925
     Else (feature 2 > 1013.395)
      If (feature 1 <= 58.19)
       Predict: 4.568024116358373
      Else (feature 1 > 58.19)
       Predict: -0.36270787813043714
  Tree 54 (weight 0.1):
    If (feature 2 <= 1001.785)
     If (feature 3 <= 63.575)
      If (feature 3 <= 60.025000000000006)
       Predict: -1.7427137986580719
      Else (feature 3 > 60.025000000000006)
       Predict: -7.776342039375448
     Else (feature 3 > 63.575)
      If (feature 0 <= 27.119999999999997)
       Predict: 0.026174663094801796
      Else (feature 0 > 27.119999999999997)
       Predict: -2.343138620856655
    Else (feature 2 > 1001.785)
     If (feature 0 <= 22.055)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.27236570115397085
      Else (feature 1 > 66.21000000000001)
       Predict: 3.164590339421908
     Else (feature 0 > 22.055)
      If (feature 0 <= 22.725)
       Predict: 2.9597120242025485
      Else (feature 0 > 22.725)
       Predict: 0.054881549113388446
  Tree 55 (weight 0.1):
    If (feature 2 <= 1028.14)
     If (feature 1 <= 45.754999999999995)
      If (feature 0 <= 26.715)
       Predict: 0.2599176823406179
      Else (feature 0 > 26.715)
       Predict: -5.549910372715466
     Else (feature 1 > 45.754999999999995)
      If (feature 0 <= 15.475000000000001)
       Predict: -2.867925531108855
      Else (feature 0 > 15.475000000000001)
       Predict: -0.0236838100703647
    Else (feature 2 > 1028.14)
     If (feature 0 <= 8.575)
      If (feature 1 <= 39.45)
       Predict: 0.7777761733424086
      Else (feature 1 > 39.45)
       Predict: -5.202001886405932
     Else (feature 0 > 8.575)
      If (feature 3 <= 68.945)
       Predict: -3.641368616696809
      Else (feature 3 > 68.945)
       Predict: -0.6008141057791702
  Tree 56 (weight 0.1):
    If (feature 3 <= 61.89)
     If (feature 0 <= 18.29)
      If (feature 2 <= 1019.025)
       Predict: -2.1632736300070996
      Else (feature 2 > 1019.025)
       Predict: 1.2430029950309498
     Else (feature 0 > 18.29)
      If (feature 1 <= 44.91)
       Predict: 3.2134324628571003
      Else (feature 1 > 44.91)
       Predict: 0.2988556025240279
    Else (feature 3 > 61.89)
     If (feature 1 <= 71.505)
      If (feature 1 <= 66.85499999999999)
       Predict: -0.09866504525517336
      Else (feature 1 > 66.85499999999999)
       Predict: 0.7271499788275563
     Else (feature 1 > 71.505)
      If (feature 1 <= 73.00999999999999)
       Predict: -2.395971400707892
      Else (feature 1 > 73.00999999999999)
       Predict: -0.16608323426526406
  Tree 57 (weight 0.1):
    If (feature 2 <= 1017.645)
     If (feature 2 <= 1014.615)
      If (feature 1 <= 43.175)
       Predict: 1.0389282601473917
      Else (feature 1 > 43.175)
       Predict: -0.3646675630154698
     Else (feature 2 > 1014.615)
      If (feature 1 <= 43.510000000000005)
       Predict: -0.6889797697082773
      Else (feature 1 > 43.510000000000005)
       Predict: 1.5908362409321974
    Else (feature 2 > 1017.645)
     If (feature 2 <= 1019.025)
      If (feature 1 <= 71.18)
       Predict: -1.6569133708803008
      Else (feature 1 > 71.18)
       Predict: 4.314967971455512
     Else (feature 2 > 1019.025)
      If (feature 3 <= 89.595)
       Predict: 0.43765066032139976
      Else (feature 3 > 89.595)
       Predict: -1.7344432288008194
  Tree 58 (weight 0.1):
    If (feature 2 <= 1004.505)
     If (feature 1 <= 70.815)
      If (feature 1 <= 59.834999999999994)
       Predict: -0.170418541124326
      Else (feature 1 > 59.834999999999994)
       Predict: -2.895038840312831
     Else (feature 1 > 70.815)
      If (feature 2 <= 1000.505)
       Predict: -1.3832637115340176
      Else (feature 2 > 1000.505)
       Predict: 1.2249185299155396
    Else (feature 2 > 1004.505)
     If (feature 0 <= 22.055)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.2418541439735617
      Else (feature 1 > 66.21000000000001)
       Predict: 3.0110894160107886
     Else (feature 0 > 22.055)
      If (feature 0 <= 23.945)
       Predict: 1.4999960684883906
      Else (feature 0 > 23.945)
       Predict: -0.042407486119460915
  Tree 59 (weight 0.1):
    If (feature 1 <= 77.42)
     If (feature 1 <= 74.915)
      If (feature 1 <= 71.505)
       Predict: 0.03983464447255206
      Else (feature 1 > 71.505)
       Predict: -0.7683021525819648
     Else (feature 1 > 74.915)
      If (feature 3 <= 72.82)
       Predict: 2.774179202497669
      Else (feature 3 > 72.82)
       Predict: -0.32979787820368633
    Else (feature 1 > 77.42)
     If (feature 0 <= 21.595)
      If (feature 0 <= 21.335)
       Predict: -13.565760771414489
      Else (feature 0 > 21.335)
       Predict: -13.954507897669714
     Else (feature 0 > 21.595)
      If (feature 0 <= 22.955)
       Predict: 10.853700477067264
      Else (feature 0 > 22.955)
       Predict: -1.4643467280880287
  Tree 60 (weight 0.1):
    If (feature 0 <= 10.395)
     If (feature 1 <= 40.629999999999995)
      If (feature 3 <= 75.545)
       Predict: -2.3127871072788375
      Else (feature 3 > 75.545)
       Predict: 0.3992391857664209
     Else (feature 1 > 40.629999999999995)
      If (feature 3 <= 89.595)
       Predict: 2.9152362525803523
      Else (feature 3 > 89.595)
       Predict: -1.4086879927580456
    Else (feature 0 > 10.395)
     If (feature 0 <= 11.885000000000002)
      If (feature 2 <= 1025.2150000000001)
       Predict: -2.2352340341897547
      Else (feature 2 > 1025.2150000000001)
       Predict: 5.952010328542228
     Else (feature 0 > 11.885000000000002)
      If (feature 0 <= 12.415)
       Predict: 3.4957190546300447
      Else (feature 0 > 12.415)
       Predict: -0.03992973893781255
  Tree 61 (weight 0.1):
    If (feature 1 <= 40.82)
     If (feature 1 <= 40.224999999999994)
      If (feature 3 <= 75.545)
       Predict: -1.2944988036912455
      Else (feature 3 > 75.545)
       Predict: 0.44866615475817667
     Else (feature 1 > 40.224999999999994)
      If (feature 2 <= 1025.2150000000001)
       Predict: 0.7826360308981203
      Else (feature 2 > 1025.2150000000001)
       Predict: 9.875672812487991
    Else (feature 1 > 40.82)
     If (feature 2 <= 1025.2150000000001)
      If (feature 0 <= 10.945)
       Predict: 1.1628421208118285
      Else (feature 0 > 10.945)
       Predict: -0.12551627485602937
     Else (feature 2 > 1025.2150000000001)
      If (feature 1 <= 41.15)
       Predict: -7.782369290305258
      Else (feature 1 > 41.15)
       Predict: -1.200273276366743
  Tree 62 (weight 0.1):
    If (feature 0 <= 22.055)
     If (feature 0 <= 21.335)
      If (feature 1 <= 66.21000000000001)
       Predict: -0.09621281022933233
      Else (feature 1 > 66.21000000000001)
       Predict: 2.680549669942832
     Else (feature 0 > 21.335)
      If (feature 1 <= 63.864999999999995)
       Predict: -3.207766197974041
      Else (feature 1 > 63.864999999999995)
       Predict: -0.14162445042909583
    Else (feature 0 > 22.055)
     If (feature 0 <= 22.725)
      If (feature 2 <= 1011.515)
       Predict: 0.5064350504779975
      Else (feature 2 > 1011.515)
       Predict: 3.659612527058891
     Else (feature 0 > 22.725)
      If (feature 3 <= 75.545)
       Predict: 0.2595183211083464
      Else (feature 3 > 75.545)
       Predict: -0.6564588168227348
  Tree 63 (weight 0.1):
    If (feature 2 <= 1017.645)
     If (feature 1 <= 44.67)
      If (feature 1 <= 44.13)
       Predict: 0.3869693354075677
      Else (feature 1 > 44.13)
       Predict: 4.723132901950317
     Else (f...

Conclusion

Wow! So our best model is in fact our Gradient Boosted Decision tree model which uses an ensemble of 120 Trees with a depth of 3 to construct a better model than the single decision tree.

Step 8: Deployment will be done later

Now that we have a predictive model it is time to deploy the model into an operational environment.

In our example, let's say we have a series of sensors attached to the power plant and a monitoring station.

The monitoring station will need close to real-time information about how much power that their station will generate so they can relay that to the utility.

For this we need to create a Spark Streaming utility that we can use for this purpose. For this you need to be introduced to basic concepts in Spark Streaming first. See http://spark.apache.org/docs/latest/streaming-programming-guide.html if you can't wait!

After deployment you will be able to use the best predictions from gradient boosed regression trees to feed a real-time dashboard or feed the utility with information on how much power the peaker plant will deliver give current conditions.

Persisting Statistical Machine Learning Models

See https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html

Let's save our best model so we can load it without having to rerun the validation and training again.

gbtModel
res117: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
gbtModel.bestModel.asInstanceOf[PipelineModel]
        .write.overwrite().save("dbfs:///databricks/driver/MyTrainedBestPipelineModel")

When it is time to deploy the trained model to serve predicitons, we cna simply reload this model and proceed as shown later.

Datasource References:

  • Pinar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615, Web Link
  • Heysem Kaya, Pinar Tüfekci , Sadik Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai) Web Link

ScaDaMaLe Course site and book

Activity Recognition from Accelerometer

  1. ETL Using SparkSQL Windows

  1. Prediction using Random Forest

This work is a simpler databricksification of Amira Lakhal's more complex framework for activity recognition:

Amira's video

See Section below on 0. Download and Load Data first.

  • Once data is loaded come back here (if you have not downloaded and loaded data into the distributed file system under your hood).
val data = sc.textFile("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv") // assumes data is loaded
data: org.apache.spark.rdd.RDD[String] = dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv MapPartitionsRDD[1] at textFile at command-561044440962565:1
data.take(5).foreach(println)
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
val dataDF = sqlContext.read    
    .format("com.databricks.spark.csv") // use spark.csv package
    .option("header", "true") // Use first line of all files as header
    .option("inferSchema", "true") // Automatically infer data types
    .option("delimiter", ",") // Specify the delimiter as ','
    .load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
val dataDFnew = spark.read.format("csv") 
  .option("inferSchema", "true") 
  .option("header", "true") 
  .option("sep", ",") 
  .load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDFnew: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
dataDFnew.printSchema()
root
 |-- user_id: string (nullable = true)
 |-- activity: string (nullable = true)
 |-- timeStampAsLong: long (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)
display(dataDF) // zp.show(dataDF)
user_id activity timeStampAsLong x y z
user_001 Jumping 1.446047227606e12 4.33079 -12.72175 -3.18118
user_001 Jumping 1.446047227671e12 0.575403 -0.727487 2.95007
user_001 Jumping 1.446047227735e12 -1.60885 3.52607 -0.1922
user_001 Jumping 1.446047227799e12 0.690364 -3.7722e-2 1.72382
user_001 Jumping 1.446047227865e12 3.44943 -1.68549 2.29862
user_001 Jumping 1.44604722793e12 1.87829 -1.91542 0.880768
user_001 Jumping 1.446047227995e12 1.57173 -5.86241 -3.75599
user_001 Jumping 1.446047228059e12 3.41111 -17.93331 0.535886
user_001 Jumping 1.446047228123e12 3.18118 -19.58108 5.74745
user_001 Jumping 1.446047228189e12 7.85626 -19.2362 0.804128
user_001 Jumping 1.446047228253e12 1.26517 -8.85139 2.18366
user_001 Jumping 1.446047228318e12 7.7239e-2 1.15021 1.53221
user_001 Jumping 1.446047228383e12 0.230521 2.0699 -1.41845
user_001 Jumping 1.446047228447e12 0.652044 -0.497565 1.76214
user_001 Jumping 1.446047228512e12 1.53341 -0.305964 1.41725
user_001 Jumping 1.446047228578e12 -1.07237 -1.95374 0.191003
user_001 Jumping 1.446047228642e12 2.75966 -13.75639 0.191003
user_001 Jumping 1.446047228707e12 7.43474 -19.58108 2.95007
user_001 Jumping 1.446047228771e12 5.63368 -19.58108 5.59417
user_001 Jumping 1.446047228836e12 5.02056 -11.72542 -1.38013
user_001 Jumping 1.4460472289e12 -2.10702 0.575403 1.07237
user_001 Jumping 1.446047228966e12 -1.30229 2.2615 -1.26517
user_001 Jumping 1.44604722903e12 1.68669 -0.957409 1.57053
user_001 Jumping 1.446047229095e12 2.60638 -0.229323 2.14534
user_001 Jumping 1.446047229159e12 1.30349 -0.152682 0.497565
user_001 Jumping 1.446047229224e12 1.64837 -8.12331 1.60885
user_001 Jumping 1.44604722929e12 0.15388 -18.46979 -1.03525
user_001 Jumping 1.446047229354e12 4.98224 -19.58108 0.995729
user_001 Jumping 1.446047229419e12 5.17384 -19.2362 0.612526
user_001 Jumping 1.446047229483e12 2.03158 -9.11964 1.34061
user_001 Jumping 1.446047229549e12 -1.95374 -1.03405 0.574206
user_001 Jumping 1.446047229612e12 0.1922 1.30349 -1.03525
user_001 Jumping 1.446047229678e12 1.64837 -0.727487 -0.307161
user_001 Jumping 1.446047229743e12 2.56806 -1.03405 0.267643
user_001 Jumping 1.446047229806e12 1.41845 -0.305964 0.727487
user_001 Jumping 1.446047229872e12 1.03525 -6.82042 0.957409
user_001 Jumping 1.446047229936e12 3.94759 -19.31284 -1.22685
user_001 Jumping 1.446047230001e12 6.13185 -19.58108 2.72014
user_001 Jumping 1.446047230066e12 7.89458 -16.9753 0.229323
user_001 Jumping 1.446047230131e12 2.6447 -3.75479 0.114362
user_001 Jumping 1.446047230195e12 -2.41358 1.91661 -5.99e-4
user_001 Jumping 1.44604723026e12 -1.95374 -0.114362 -0.268841
user_001 Jumping 1.446047230325e12 -0.229323 -1.95374 2.75846
user_001 Jumping 1.44604723039e12 2.68302 0.268841 0.535886
user_001 Jumping 1.446047230455e12 1.15021 -1.41725 0.880768
user_001 Jumping 1.446047230519e12 2.98958 -11.53382 -0.920286
user_001 Jumping 1.446047230584e12 8.00954 -19.58108 -0.1922
user_001 Jumping 1.446047230649e12 7.31978 -19.58108 2.52854
user_001 Jumping 1.446047230713e12 5.44208 -13.56479 -0.498763
user_001 Jumping 1.446047230779e12 0.728685 -0.420925 1.68549
user_001 Jumping 1.446047230842e12 1.18853 1.41845 -2.22318
user_001 Jumping 1.446047230907e12 0.15388 -1.80046 2.03038
user_001 Jumping 1.446047230972e12 -3.7722e-2 1.07357 -0.958607
user_001 Jumping 1.446047231038e12 0.230521 0.728685 -0.690364
user_001 Jumping 1.446047231102e12 0.805325 -1.14901 1.53221
user_001 Jumping 1.446047231167e12 5.17384 -9.15796 -2.91294
user_001 Jumping 1.446047231231e12 8.00954 -19.54276 1.49389
user_001 Jumping 1.446047231296e12 11.61165 -19.58108 4.25296
user_001 Jumping 1.446047231361e12 7.89458 -18.35483 3.71647
user_001 Jumping 1.446047231426e12 0.537083 -3.21831 0.420925
user_001 Jumping 1.44604723149e12 -1.91542 4.10087 -2.6447
user_001 Jumping 1.446047231555e12 -0.919089 -0.382604 0.114362
user_001 Jumping 1.44604723162e12 0.613724 -0.842448 1.57053
user_001 Jumping 1.446047231684e12 1.38013 -0.459245 0.305964
user_001 Jumping 1.446047231749e12 2.29982 -1.49389 -1.26517
user_001 Jumping 1.446047231814e12 4.94392 -10.95901 -0.498763
user_001 Jumping 1.446047231878e12 3.75599 -19.27452 -1.76333
user_001 Jumping 1.446047231944e12 7.70298 -19.58108 -2.95126
user_001 Jumping 1.446047232008e12 6.55337 -17.51178 -1.91661
user_001 Jumping 1.446047232073e12 2.10822 -2.03038 -0.268841
user_001 Jumping 1.446047232138e12 -1.68549 3.67935 -0.881966
user_001 Jumping 1.446047232203e12 0.15388 -0.114362 7.6042e-2
user_001 Jumping 1.446047232267e12 2.79798 -1.95374 0.689167
user_001 Jumping 1.446047232332e12 1.45677 -0.957409 0.420925
user_001 Jumping 1.446047232397e12 1.18853 -2.95007 0.650847
user_001 Jumping 1.446047232462e12 3.44943 -13.87135 -3.71767
user_001 Jumping 1.446047232526e12 3.79431 -19.58108 -0.345482
user_001 Jumping 1.446047232591e12 6.09353 -19.58108 2.87342
user_001 Jumping 1.446047232655e12 4.48408 -14.3312 -0.498763
user_001 Jumping 1.44604723272e12 -3.7722e-2 -1.68549 2.6435
user_001 Jumping 1.446047232785e12 0.613724 2.68302 -2.37646
user_001 Jumping 1.44604723285e12 0.996927 -0.497565 0.497565
user_001 Jumping 1.446047232915e12 0.15388 -0.689167 1.34061
user_001 Jumping 1.446047232979e12 1.07357 1.07357 -2.60638
user_001 Jumping 1.446047233044e12 2.37646 -3.29495 -1.45677
user_001 Jumping 1.446047233109e12 2.22318 -16.09393 1.37893
user_001 Jumping 1.446047233173e12 6.82161 -19.58108 3.7722e-2
user_001 Jumping 1.446047233238e12 8.39275 -19.54276 1.22565
user_001 Jumping 1.446047233303e12 1.22685 -9.50284 -0.307161
user_001 Jumping 1.446047233368e12 1.83997 -0.152682 1.41725
user_001 Jumping 1.446047233432e12 -0.765807 0.996927 -1.91661
user_001 Jumping 1.446047233497e12 0.728685 -0.612526 0.152682
user_001 Jumping 1.446047233562e12 0.996927 -0.305964 0.995729
user_001 Jumping 1.446047233626e12 1.22685 -0.305964 3.7722e-2
user_001 Jumping 1.446047233692e12 1.91661 -2.95007 -0.345482
user_001 Jumping 1.446047233755e12 4.75232 -14.63776 -3.67935
user_001 Jumping 1.446047233821e12 4.56072 -19.58108 -2.6447
user_001 Jumping 1.446047233886e12 10.69197 -19.38948 -5.94025
user_001 Jumping 1.44604723395e12 3.10454 -10.42253 -2.22318
user_001 Jumping 1.446047234015e12 -0.152682 1.61005 1.80046
user_001 Jumping 1.44604723408e12 -0.267643 1.83997 -0.958607
user_001 Jumping 1.446047234145e12 1.99325 -0.842448 -0.230521
user_001 Jumping 1.44604723421e12 2.79798 -0.114362 0.919089
user_001 Jumping 1.446047234273e12 1.11189 -0.152682 1.83878
user_001 Jumping 1.446047234339e12 2.75966 -5.05768 -0.537083
user_001 Jumping 1.446047234403e12 4.33079 -16.82202 -3.06622
user_001 Jumping 1.446047234468e12 6.89825 -19.58108 -4.25415
user_001 Jumping 1.446047234533e12 11.49669 -19.50444 -2.52974
user_001 Jumping 1.446047234598e12 6.20849 -9.08132 -1.80165
user_001 Jumping 1.446047234663e12 0.805325 1.15021 -0.15388
user_001 Jumping 1.446047234726e12 -1.76214 2.14654 -0.460442
user_001 Jumping 1.446047234792e12 5.99e-4 -1.11069 -0.230521
user_001 Jumping 1.446047234857e12 0.652044 0.230521 -0.920286
user_001 Jumping 1.446047234922e12 1.26517 -0.114362 -5.99e-4
user_001 Jumping 1.446047234986e12 5.36544 -4.09967 -0.537083
user_001 Jumping 1.446047235051e12 6.51505 -17.05194 -3.56439
user_001 Jumping 1.446047235115e12 6.93658 -19.58108 -2.75966
user_001 Jumping 1.446047235181e12 7.89458 -19.58108 -3.18118
user_001 Jumping 1.446047235245e12 1.95493 -9.6178 -1.34181
user_001 Jumping 1.44604723531e12 -2.18366 3.14286 -0.1922
user_001 Jumping 1.446047235375e12 -2.87342 1.99325 -1.76333
user_001 Jumping 1.446047235439e12 1.03525 -1.80046 1.64717
user_001 Jumping 1.446047235504e12 1.95493 -0.689167 1.49389
user_001 Jumping 1.446047235569e12 1.26517 0.383802 -0.307161
user_001 Jumping 1.446047235633e12 1.18853 -2.72014 1.41725
user_001 Jumping 1.446047235698e12 5.71033 -16.51545 -2.03158
user_001 Jumping 1.446047235763e12 6.55337 -19.58108 0.995729
user_001 Jumping 1.446047235828e12 10.577 -19.4278 -1.57173
user_001 Jumping 1.446047235892e12 2.79798 -9.88604 -0.843646
user_001 Jumping 1.446047235957e12 0.652044 -3.7722e-2 1.14901
user_001 Jumping 1.446047236022e12 0.843646 1.95493 -2.14654
user_001 Jumping 1.446047236086e12 0.15388 -0.305964 0.995729
user_001 Jumping 1.446047236152e12 0.767005 -0.114362 0.804128
user_001 Jumping 1.446047236215e12 -0.650847 -0.650847 -0.230521
user_001 Jumping 1.446047236281e12 3.56439 -5.90073 0.727487
user_001 Jumping 1.446047236346e12 7.05154 -17.97163 -1.61005
user_001 Jumping 1.44604723641e12 9.46572 -19.58108 2.10702
user_001 Jumping 1.446047236475e12 7.58802 -18.54643 3.10335
user_001 Jumping 1.44604723654e12 0.767005 -6.28393 0.689167
user_001 Jumping 1.446047236604e12 0.307161 2.29982 -0.498763
user_001 Jumping 1.446047236669e12 0.11556 -0.114362 -1.11189
user_001 Jumping 1.446047236734e12 2.2615 -1.45557 2.14534
user_001 Jumping 1.446047236798e12 1.80165 0.345482 0.765807
user_001 Jumping 1.446047236863e12 -0.650847 -0.305964 -1.15021
user_001 Jumping 1.446047236928e12 3.48775 -7.31858 -0.728685
user_001 Jumping 1.446047236992e12 5.32712 -18.92964 0.114362
user_001 Jumping 1.446047237057e12 10.34708 -19.58108 2.37526
user_001 Jumping 1.446047237122e12 5.97857 -17.78002 0.344284
user_001 Jumping 1.446047237187e12 2.8363 -3.25663 0.152682
user_001 Jumping 1.446047237252e12 -0.995729 3.37279 -1.64837
user_001 Jumping 1.446047237317e12 -1.64717 -0.650847 1.41725
user_001 Jumping 1.446047237381e12 1.30349 -0.152682 1.45557
user_001 Jumping 1.446047237446e12 2.79798 5.99e-4 -0.460442
user_001 Jumping 1.44604723751e12 1.41845 -0.459245 -1.38013
user_001 Jumping 1.446047237575e12 3.90927 -6.62882 -1.49509
user_001 Jumping 1.44604723764e12 4.98224 -19.27452 1.57053
user_001 Jumping 1.446047237705e12 12.22478 -19.58108 2.0687
user_001 Jumping 1.446047237769e12 4.5224 -16.74538 -0.383802
user_001 Jumping 1.446047237834e12 0.690364 -0.880768 -0.537083
user_001 Jumping 1.446047237899e12 1.26517 2.18486 -1.03525
user_001 Jumping 1.446047237964e12 2.6447 -3.7722e-2 1.18733
user_001 Jumping 1.446047238028e12 2.56806 -0.114362 1.03405
user_001 Jumping 1.446047238094e12 -1.14901 0.11556 -0.307161
user_001 Jumping 1.446047238157e12 -0.574206 -0.114362 -0.920286
user_001 Jumping 1.446047238223e12 2.60638 -3.40991 -0.422122
user_001 Jumping 1.446047238287e12 6.32345 -17.62674 -0.728685
user_001 Jumping 1.446047238352e12 10.53868 -19.58108 0.919089
user_001 Jumping 1.446047238417e12 7.85626 -18.20155 0.995729
user_001 Jumping 1.446047238481e12 -0.152682 -1.41725 -1.22685
user_001 Jumping 1.446047238546e12 1.26517 2.22318 -1.87829
user_001 Jumping 1.446047238611e12 0.537083 -0.497565 1.76214
user_001 Jumping 1.446047238676e12 1.95493 0.230521 1.60885
user_001 Jumping 1.446047238741e12 0.920286 0.537083 -0.422122
user_001 Jumping 1.446047238806e12 5.99e-4 -0.650847 0.497565
user_001 Jumping 1.44604723887e12 1.64837 -1.99206 -1.72501
user_001 Jumping 1.446047238935e12 5.0972 -14.75272 -1.41845
user_001 Jumping 1.446047239e12 10.50036 -19.58108 1.91542
user_001 Jumping 1.446047239064e12 8.16283 -19.58108 1.95374
user_001 Jumping 1.446047239129e12 3.56439 -9.4262 -1.22685
user_001 Jumping 1.446047239194e12 0.920286 2.79798 -1.34181
user_001 Jumping 1.446047239259e12 0.1922 0.652044 -0.537083
user_001 Jumping 1.446047239324e12 1.64837 -0.957409 1.41725
user_001 Jumping 1.446047239388e12 1.99325 -0.191003 0.957409
user_001 Jumping 1.446047239453e12 1.26517 -0.305964 -5.99e-4
user_001 Jumping 1.446047239518e12 2.4531 -4.17632 -1.34181
user_001 Jumping 1.446047239583e12 4.44576 -16.05561 -0.537083
user_001 Jumping 1.446047239647e12 9.58068 -19.58108 1.53221
user_001 Jumping 1.446047239712e12 8.04786 -19.2362 2.0687
user_001 Jumping 1.446047239777e12 1.80165 -6.01569 -0.422122
user_001 Jumping 1.446047239841e12 0.11556 2.10822 -2.6447
user_001 Jumping 1.446047239906e12 0.345482 -0.612526 -1.22685
user_001 Jumping 1.446047239971e12 1.45677 -0.880768 2.2603
user_001 Jumping 1.446047240036e12 0.843646 1.11189 -0.537083
user_001 Jumping 1.4460472401e12 0.996927 3.8919e-2 -0.575403
user_001 Jumping 1.446047240166e12 3.29615 -3.98471 -0.422122
user_001 Jumping 1.446047240231e12 5.21216 -16.7837 -0.613724
user_001 Jumping 1.446047240295e12 7.39642 -19.58108 1.11069
user_001 Jumping 1.44604724036e12 10.23212 -19.58108 0.459245
user_001 Jumping 1.446047240424e12 1.80165 -12.14694 -1.22685
user_001 Jumping 1.446047240489e12 0.575403 1.83997 3.7722e-2
user_001 Jumping 1.446047240553e12 -0.152682 2.22318 -3.0279
user_001 Jumping 1.446047240618e12 0.268841 -1.91542 3.06503
user_001 Jumping 1.446047240683e12 1.72501 -0.535886 1.83878
user_001 Jumping 1.446047240748e12 -1.18733 0.996927 -1.26517
user_001 Jumping 1.446047240813e12 0.920286 -0.535886 -1.91661
user_001 Jumping 1.446047240878e12 4.40743 -11.61046 0.459245
user_001 Jumping 1.446047240941e12 6.78329 -19.58108 4.82776
user_001 Jumping 1.446047241007e12 9.35075 -19.58108 5.74745
user_001 Jumping 1.446047241072e12 5.71033 -15.74905 -1.15021
user_001 Jumping 1.446047241136e12 -1.57053 -1.80046 -0.15388
user_001 Jumping 1.446047241201e12 -0.267643 2.18486 -2.14654
user_001 Jumping 1.446047241266e12 2.22318 -1.80046 1.80046
user_001 Jumping 1.44604724133e12 -1.34061 0.767005 -2.03158
user_001 Jumping 1.446047241395e12 -0.382604 -0.842448 0.382604
user_001 Jumping 1.446047241459e12 -0.765807 -1.41725 7.6042e-2
user_001 Jumping 1.446047241525e12 5.97857 -10.34589 -1.49509
user_001 Jumping 1.446047241589e12 11.34341 -19.58108 6.13065
user_001 Jumping 1.446047241654e12 10.15548 -19.58108 8.58315
user_001 Jumping 1.446047241718e12 4.67568 -11.61046 -0.307161
user_001 Jumping 1.446047241783e12 -1.37893 -0.650847 0.459245
user_001 Jumping 1.446047241849e12 -0.919089 2.6447 -3.14286
user_001 Jumping 1.446047241913e12 1.07357 -1.03405 -0.613724
user_001 Jumping 1.446047241977e12 0.15388 -0.919089 2.18366
user_001 Jumping 1.446047242042e12 -0.995729 0.460442 0.305964
user_001 Jumping 1.446047242107e12 2.03158 -0.382604 7.6042e-2
user_001 Jumping 1.446047242172e12 6.93658 -10.84405 -0.422122
user_001 Jumping 1.446047242237e12 13.79591 -19.58108 1.49389
user_001 Jumping 1.446047242302e12 15.86521 -19.50444 -1.72501
user_001 Jumping 1.446047242366e12 4.79064 -12.03198 1.8771
user_001 Jumping 1.446047242431e12 -0.497565 1.45677 -4.10087
user_001 Jumping 1.446047242495e12 0.11556 0.613724 -1.30349
user_001 Jumping 1.44604724256e12 -0.459245 -0.305964 2.56686
user_001 Jumping 1.446047242625e12 2.29982 1.18853 -0.767005
user_001 Jumping 1.44604724269e12 1.38013 -0.305964 -1.87829
user_001 Jumping 1.446047242754e12 2.52974 -2.75846 0.152682
user_001 Jumping 1.446047242819e12 5.32712 -13.21991 -1.34181
user_001 Jumping 1.446047242884e12 10.50036 -19.58108 1.8771
user_001 Jumping 1.446047242948e12 13.02951 -19.58108 -0.498763
user_001 Jumping 1.446047243013e12 4.33079 -10.34589 -2.4531
user_001 Jumping 1.446047243078e12 0.613724 0.498763 2.49022
user_001 Jumping 1.446047243143e12 0.307161 1.53341 -2.49142
user_001 Jumping 1.446047243207e12 7.7239e-2 -0.344284 1.91542
user_001 Jumping 1.446047243272e12 2.03158 0.1922 -5.99e-4
user_001 Jumping 1.446047243337e12 1.22685 0.230521 -1.87829
user_001 Jumping 1.446047243401e12 0.537083 -1.26397 0.535886
user_001 Jumping 1.446047243467e12 5.2888 -11.22725 0.689167
user_001 Jumping 1.446047243531e12 11.57333 -19.58108 0.344284
user_001 Jumping 1.446047243596e12 14.524 -19.58108 2.33694
user_001 Jumping 1.446047243661e12 3.18118 -11.8787 0.191003
user_001 Jumping 1.446047243725e12 -1.07237 1.15021 0.152682
user_001 Jumping 1.44604724379e12 0.422122 1.95493 -1.87829
user_001 Jumping 1.446047243855e12 -0.535886 -0.957409 1.18733
user_001 Jumping 1.44604724392e12 3.60271 -0.114362 0.574206
user_001 Jumping 1.446047243984e12 2.87462 -0.535886 -0.268841
user_001 Jumping 1.446047244049e12 2.2615 -1.53221 -0.11556
user_001 Jumping 1.446047244114e12 6.85994 -10.61413 -1.11189
user_001 Jumping 1.446047244178e12 7.97122 -19.58108 -7.7239e-2
user_001 Jumping 1.446047244243e12 14.86888 -19.58108 -1.41845
user_001 Jumping 1.446047244308e12 8.50771 -14.48448 -0.613724
user_001 Jumping 1.446047244373e12 -0.535886 -1.18733 1.64717
user_001 Jumping 1.446047244437e12 -4.5212 3.37279 0.191003
user_001 Jumping 1.446047244502e12 -1.72382 0.690364 0.191003
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user_003 Jumping 1.446047778164e12 -2.14534 -6.74378 -2.22318
user_003 Jumping 1.446047778229e12 -2.22198 -6.01569 -2.2615
user_003 Jumping 1.446047778293e12 -3.63983 -9.04299 -2.6447
user_003 Jumping 1.446047778358e12 -4.82776 -17.09026 -4.02423
user_003 Jumping 1.446047778422e12 -5.82409 -19.46612 -6.70665
user_003 Jumping 1.446047778488e12 0.805325 -10.92069 -1.49509
user_003 Jumping 1.446047778552e12 -4.67448 1.22685 1.64717
user_003 Jumping 1.446047778616e12 -1.37893 0.460442 -2.18486
user_003 Jumping 1.446047778682e12 0.1922 -0.650847 -7.7239e-2
user_003 Jumping 1.446047778746e12 -0.842448 0.11556 -1.26517
user_003 Jumping 1.446047778811e12 -1.26397 -11.91702 -3.64103
user_003 Jumping 1.446047778876e12 -1.41725 -19.58108 -0.767005
user_003 Jumping 1.44604777894e12 -1.76214 -15.44249 -2.68302
user_003 Jumping 1.446047779006e12 -0.574206 -9.00468 -1.76333
user_003 Jumping 1.446047779071e12 -3.14167 -4.36792 -1.68669
user_003 Jumping 1.446047779135e12 -3.37159 -4.3296 -2.4531
user_003 Jumping 1.4460477792e12 -4.06135 -4.75112 -3.10454
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user_002 Standing 1.446309441655e12 -3.06503 -8.96635 -0.383802
user_002 Standing 1.446309441664e12 -3.17999 -9.2346 -0.460442
user_002 Standing 1.446309441672e12 -3.10335 -9.11964 -0.422122
user_002 Standing 1.44630944168e12 -3.10335 -9.19628 -0.345482
user_002 Standing 1.446309441688e12 -3.29495 -9.19628 -0.422122
user_002 Standing 1.446309441696e12 -3.21831 -8.96635 -0.307161
user_002 Standing 1.446309441705e12 -3.14167 -9.04299 -0.460442
user_002 Standing 1.446309441713e12 -3.25663 -9.00468 -0.422122
user_002 Standing 1.446309441721e12 -3.25663 -9.00468 -0.575403
user_002 Standing 1.446309441729e12 -3.29495 -9.04299 -0.345482
user_002 Standing 1.446309441737e12 -3.25663 -9.00468 -0.575403
user_002 Standing 1.446309441745e12 -3.17999 -8.96635 -0.537083
user_002 Standing 1.446309441752e12 -3.25663 -9.11964 -0.422122
user_002 Standing 1.446309441761e12 -3.21831 -9.11964 -0.307161
user_002 Standing 1.446309441769e12 -3.10335 -8.92803 -0.460442
user_002 Standing 1.446309441777e12 -3.10335 -9.04299 -0.460442
user_002 Standing 1.446309441785e12 -3.21831 -9.11964 -0.345482
user_002 Standing 1.446309441793e12 -3.17999 -9.2346 -0.307161
user_002 Standing 1.446309441802e12 -3.17999 -9.08132 -0.383802
user_002 Standing 1.446309441809e12 -3.17999 -9.04299 -0.498763
user_002 Standing 1.446309441818e12 -3.06503 -9.04299 -0.383802
user_002 Standing 1.446309441825e12 -3.14167 -8.96635 -0.383802
user_002 Standing 1.446309441833e12 -3.25663 -8.88971 -0.422122
user_002 Standing 1.446309441842e12 -3.17999 -8.88971 -0.537083
user_002 Standing 1.44630944185e12 -3.21831 -8.96635 -0.422122
user_002 Standing 1.446309441858e12 -3.02671 -8.81307 -0.460442
user_002 Standing 1.446309441867e12 -3.17999 -8.92803 -0.460442
user_002 Standing 1.446309441875e12 -3.06503 -8.85139 -0.268841
user_002 Standing 1.446309441882e12 -3.29495 -8.85139 -0.345482
user_002 Standing 1.446309441891e12 -3.33327 -8.88971 -0.268841
user_002 Standing 1.446309441899e12 -3.33327 -8.81307 -0.1922
user_002 Standing 1.446309441907e12 -3.17999 -8.77475 -0.230521
user_002 Standing 1.446309441915e12 -3.17999 -9.08132 -3.8919e-2
user_002 Standing 1.446309441923e12 -3.14167 -9.19628 -0.15388
user_002 Standing 1.446309441931e12 -3.21831 -9.08132 -0.1922
user_002 Standing 1.446309441939e12 -3.06503 -9.00468 -0.268841
user_002 Standing 1.446309441947e12 -3.29495 -8.92803 -0.11556
user_002 Standing 1.446309441956e12 -3.21831 -9.00468 -0.307161
user_002 Standing 1.446309441964e12 -3.06503 -8.88971 -0.268841
user_002 Standing 1.446309441971e12 -3.25663 -9.00468 -0.15388
user_002 Standing 1.446309441979e12 -3.25663 -9.08132 -0.268841
user_002 Standing 1.446309441987e12 -3.29495 -8.92803 -0.1922
user_002 Standing 1.446309441995e12 -3.33327 -8.96635 -0.268841
user_002 Standing 1.446309442004e12 -3.21831 -8.88971 -0.11556
user_002 Standing 1.446309442012e12 -3.25663 -9.11964 -5.99e-4
user_002 Standing 1.44630944202e12 -3.06503 -9.00468 -5.99e-4
user_002 Standing 1.446309442028e12 -3.06503 -9.11964 -3.8919e-2
user_002 Standing 1.446309442036e12 -3.02671 -9.2346 -3.8919e-2
user_002 Standing 1.446309442045e12 -2.95007 -9.19628 -0.15388
user_002 Standing 1.446309442052e12 -2.91174 -9.04299 -0.1922
user_002 Standing 1.446309442061e12 -2.91174 -9.19628 -5.99e-4
user_002 Standing 1.446309442068e12 -2.95007 -9.08132 -5.99e-4
user_002 Standing 1.446309442077e12 -3.10335 -9.08132 -7.7239e-2
user_002 Standing 1.446309442085e12 -2.98839 -9.00468 -5.99e-4
user_002 Standing 1.446309442092e12 -3.10335 -9.15796 -0.268841
user_002 Standing 1.446309442157e12 -3.02671 -8.92803 -7.7239e-2
user_002 Standing 1.446309442223e12 -3.14167 -8.92803 -3.8919e-2
user_002 Standing 1.446309442287e12 -2.87342 -9.08132 -0.268841
user_002 Standing 1.446309442352e12 -2.72014 -9.11964 -7.7239e-2
user_002 Standing 1.446309442417e12 -2.56686 -9.19628 -3.8919e-2
user_002 Standing 1.446309442481e12 -2.6435 -9.11964 3.7722e-2
user_002 Standing 1.446309442547e12 -2.56686 -9.11964 -7.7239e-2
user_002 Standing 1.446309442611e12 -2.60518 -9.11964 -5.99e-4
user_002 Standing 1.446309442675e12 -2.4519 -9.2346 -5.99e-4
user_002 Standing 1.44630944274e12 -2.41358 -9.11964 -5.99e-4
user_002 Standing 1.446309442805e12 -2.41358 -9.08132 -7.7239e-2
user_002 Standing 1.44630944287e12 -2.33694 -9.15796 -7.7239e-2
user_002 Standing 1.446309442935e12 -2.37526 -9.15796 -5.99e-4
user_002 Standing 1.446309443e12 -2.29862 -9.19628 7.6042e-2
user_002 Standing 1.446309443064e12 -2.2603 -9.2346 -0.11556
user_002 Standing 1.446309443129e12 -2.22198 -9.2346 -0.11556
user_002 Standing 1.446309443194e12 -2.10702 -9.27292 -3.8919e-2
user_002 Standing 1.446309443258e12 -2.18366 -9.15796 3.7722e-2
user_002 Standing 1.446309443323e12 -2.2603 -9.19628 -0.11556
user_002 Standing 1.446309443388e12 -2.2603 -9.15796 -5.99e-4
user_002 Standing 1.446309443453e12 -2.10702 -9.2346 -5.99e-4
user_002 Standing 1.446309443517e12 -2.03038 -9.31124 -3.8919e-2
user_002 Standing 1.446309443582e12 -2.14534 -9.2346 -3.8919e-2
user_002 Standing 1.446309443647e12 -2.29862 -9.19628 -5.99e-4
user_002 Standing 1.446309443711e12 -2.29862 -9.19628 3.7722e-2
user_002 Standing 1.446309443776e12 -2.18366 -9.2346 -3.8919e-2
user_002 Standing 1.446309443841e12 -2.10702 -9.19628 3.7722e-2
user_002 Standing 1.446309443906e12 -2.10702 -9.15796 3.7722e-2
user_002 Standing 1.446309443971e12 -2.29862 -9.15796 0.114362
user_002 Standing 1.446309444036e12 -2.03038 -9.27292 -5.99e-4
user_002 Standing 1.4463094441e12 -2.10702 -9.19628 7.6042e-2
user_002 Standing 1.446309444165e12 -2.14534 -9.2346 3.7722e-2
user_002 Standing 1.44630944423e12 -2.22198 -9.19628 7.6042e-2
user_002 Standing 1.446309444295e12 -2.33694 -9.19628 0.191003
user_002 Standing 1.446309444359e12 -2.10702 -9.31124 0.114362
user_002 Standing 1.446309444424e12 -2.0687 -9.2346 -5.99e-4
user_002 Standing 1.446309444489e12 -2.03038 -9.2346 0.114362
user_002 Standing 1.446309444554e12 -2.41358 -9.00468 0.191003
user_002 Standing 1.446309444618e12 -2.37526 -9.19628 0.267643
user_002 Standing 1.446309444683e12 -2.14534 -9.2346 7.6042e-2
user_002 Standing 1.446309444748e12 -2.0687 -9.27292 7.6042e-2
user_002 Standing 1.446309444812e12 -2.33694 -9.2346 0.229323
user_002 Standing 1.446309444877e12 -2.2603 -9.19628 0.229323
user_002 Standing 1.446309444941e12 -2.18366 -9.2346 0.152682
user_002 Standing 1.446309445007e12 -2.22198 -9.2346 0.229323
user_002 Standing 1.446309445072e12 -2.22198 -9.15796 0.191003
user_002 Standing 1.446309445137e12 -2.10702 -9.27292 0.191003
user_002 Standing 1.446309445202e12 -2.10702 -9.31124 0.191003
user_002 Standing 1.446309445265e12 -1.91542 -9.34956 0.191003
user_002 Standing 1.446309445331e12 -1.83878 -9.2346 0.191003
user_002 Standing 1.446309445395e12 -1.91542 -9.08132 0.305964
user_002 Standing 1.446309445459e12 -1.95374 -9.19628 0.152682
user_002 Standing 1.446309445525e12 -1.83878 -9.34956 7.6042e-2
user_002 Standing 1.44630944559e12 -1.80046 -9.31124 7.6042e-2
user_002 Standing 1.446309445654e12 -1.68549 -9.38788 0.267643
user_002 Standing 1.446309445719e12 -1.80046 -9.19628 0.267643
user_002 Standing 1.446309445784e12 -1.76214 -9.34956 -5.99e-4
user_002 Standing 1.446309445849e12 -1.91542 -9.2346 0.152682
user_002 Standing 1.446309445913e12 -1.80046 -9.27292 0.152682
user_002 Standing 1.446309445978e12 -1.76214 -9.27292 0.152682
user_002 Standing 1.446309446043e12 -1.83878 -9.31124 0.152682
user_002 Standing 1.446309446108e12 -1.83878 -9.2346 0.152682
user_002 Standing 1.446309446173e12 -1.8771 -9.27292 0.229323
user_002 Standing 1.446309446237e12 -1.8771 -9.27292 0.229323
user_002 Standing 1.446309446302e12 -1.8771 -9.2346 0.191003
user_002 Standing 1.446309446367e12 -1.8771 -9.31124 0.191003
user_002 Standing 1.446309446432e12 -1.8771 -9.31124 0.152682
user_002 Standing 1.446309446496e12 -1.91542 -9.27292 0.191003
user_002 Standing 1.446309446561e12 -1.8771 -9.2346 0.229323
user_002 Standing 1.446309446625e12 -1.83878 -9.27292 0.152682
user_002 Standing 1.446309446691e12 -1.91542 -9.27292 7.6042e-2
user_002 Standing 1.446309446755e12 -1.8771 -9.31124 0.267643
user_002 Standing 1.44630944682e12 -1.80046 -9.27292 0.267643
user_002 Standing 1.446309446884e12 -1.80046 -9.27292 0.152682
user_002 Standing 1.44630944695e12 -1.72382 -9.31124 7.6042e-2
user_002 Standing 1.446309447014e12 -1.68549 -9.31124 7.6042e-2
user_002 Standing 1.446309447079e12 -1.83878 -9.31124 0.267643
user_002 Standing 1.446309447144e12 -1.8771 -9.27292 0.152682
user_002 Standing 1.446309447209e12 -1.76214 -9.19628 0.152682
user_002 Standing 1.446309447273e12 -1.72382 -9.27292 0.152682
user_002 Standing 1.446309447338e12 -1.83878 -9.27292 0.267643
user_002 Standing 1.446309447403e12 -1.8771 -9.31124 0.229323
user_002 Standing 1.446309447467e12 -1.83878 -9.31124 7.6042e-2
user_002 Standing 1.446309447532e12 -1.68549 -9.34956 7.6042e-2
user_002 Standing 1.446309447597e12 -1.68549 -9.34956 0.152682
user_002 Standing 1.446309447662e12 -1.72382 -9.2346 0.191003
user_002 Standing 1.446309447726e12 -1.8771 -9.31124 0.152682
user_002 Standing 1.446309447791e12 -1.80046 -9.31124 0.152682
user_002 Standing 1.446309447855e12 -1.64717 -9.31124 0.114362
user_002 Standing 1.446309447921e12 -1.60885 -9.27292 0.114362
user_002 Standing 1.446309447986e12 -1.68549 -9.27292 0.229323
user_002 Standing 1.44630944805e12 -1.76214 -9.34956 0.191003
user_002 Standing 1.446309448115e12 -1.72382 -9.34956 0.191003
user_002 Standing 1.44630944818e12 -1.72382 -9.27292 0.267643
user_002 Standing 1.446309448245e12 -1.64717 -9.31124 0.229323
user_002 Standing 1.446309448309e12 -1.64717 -9.27292 0.152682
user_002 Standing 1.446309448374e12 -1.72382 -9.31124 0.229323
user_002 Standing 1.446309448439e12 -1.72382 -9.34956 0.267643
user_002 Standing 1.446309448504e12 -1.72382 -9.2346 0.191003
user_002 Standing 1.446309448569e12 -1.64717 -9.31124 0.152682
user_002 Standing 1.446309448633e12 -1.72382 -9.27292 0.191003
user_002 Standing 1.446309448698e12 -1.68549 -9.27292 0.191003
user_002 Standing 1.446309448763e12 -1.68549 -9.27292 0.152682
user_002 Standing 1.446309448827e12 -1.64717 -9.31124 0.267643
user_002 Standing 1.446309448892e12 -1.64717 -9.34956 0.229323
user_002 Standing 1.446309448957e12 -1.60885 -9.31124 7.6042e-2
user_002 Standing 1.446309449022e12 -1.60885 -9.34956 0.152682
user_002 Standing 1.446309449087e12 -1.68549 -9.31124 0.229323
user_002 Standing 1.446309449151e12 -1.76214 -9.27292 0.229323
user_002 Standing 1.446309449216e12 -1.68549 -9.31124 0.152682
user_002 Standing 1.446309449281e12 -1.68549 -9.31124 7.6042e-2
user_002 Standing 1.446309449346e12 -1.72382 -9.2346 0.191003
user_002 Standing 1.44630944941e12 -1.64717 -9.34956 0.267643
user_002 Standing 1.446309449475e12 -1.76214 -9.27292 0.191003
user_002 Standing 1.44630944954e12 -1.64717 -9.31124 -5.99e-4
user_002 Standing 1.446309449605e12 -1.64717 -9.31124 0.229323
user_002 Standing 1.446309449669e12 -1.68549 -9.27292 0.229323
user_002 Standing 1.446309449734e12 -1.68549 -9.31124 7.6042e-2
user_002 Standing 1.446309449799e12 -1.76214 -9.31124 0.114362
user_002 Standing 1.446309449864e12 -1.76214 -9.27292 0.229323
user_002 Standing 1.446309449928e12 -1.72382 -9.31124 0.229323
user_002 Standing 1.446309449993e12 -1.72382 -9.34956 0.152682
user_002 Standing 1.446309450057e12 -1.72382 -9.34956 0.114362
user_002 Standing 1.446309450123e12 -1.72382 -9.31124 0.267643
user_002 Standing 1.446309450188e12 -1.68549 -9.34956 0.152682
user_002 Standing 1.446309450252e12 -1.83878 -9.31124 0.114362
user_002 Standing 1.446309450317e12 -1.68549 -9.34956 0.191003
user_002 Standing 1.446309450381e12 -1.72382 -9.31124 0.191003
user_002 Standing 1.446309450446e12 -1.68549 -9.34956 3.7722e-2
user_002 Standing 1.446309450511e12 -1.68549 -9.27292 0.229323
user_002 Standing 1.446309450576e12 -1.76214 -9.31124 0.229323
user_002 Standing 1.446309450641e12 -1.72382 -9.31124 0.152682
user_002 Standing 1.446309450705e12 -1.64717 -9.27292 0.152682
user_002 Standing 1.44630945077e12 -1.72382 -9.34956 0.114362
user_002 Standing 1.446309450835e12 -1.83878 -9.2346 0.191003
dataDF.count()
res5: Long = 13679
dataDF.select($"user_id").distinct().show()
+--------+
| user_id|
+--------+
|user_002|
|user_006|
|user_005|
|user_001|
|user_007|
|user_003|
|user_004|
+--------+
dataDF.select($"activity").distinct().show()
+--------+
|activity|
+--------+
| Sitting|
| Walking|
| Jumping|
|Standing|
| Jogging|
+--------+
val sDF = dataDF.sample(false,0.105, 12345L)
sDF.count
sDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 4 more fields]
res8: Long = 1427
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong|       x|        y|       z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping|  1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping|  1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping|  1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping|  1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping|  1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong|       x|        y|       z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping|  1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping|  1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping|  1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping|  1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping|  1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
display(dataDF.sample(false,0.1))
user_id activity timeStampAsLong x y z
user_001 Jumping 1.446047227799e12 0.690364 -3.7722e-2 1.72382
user_001 Jumping 1.44604722793e12 1.87829 -1.91542 0.880768
user_001 Jumping 1.446047227995e12 1.57173 -5.86241 -3.75599
user_001 Jumping 1.446047228383e12 0.230521 2.0699 -1.41845
user_001 Jumping 1.446047228512e12 1.53341 -0.305964 1.41725
user_001 Jumping 1.446047228707e12 7.43474 -19.58108 2.95007
user_001 Jumping 1.446047228966e12 -1.30229 2.2615 -1.26517
user_001 Jumping 1.446047230195e12 -2.41358 1.91661 -5.99e-4
user_001 Jumping 1.446047230584e12 8.00954 -19.58108 -0.1922
user_001 Jumping 1.446047231296e12 11.61165 -19.58108 4.25296
user_001 Jumping 1.446047233562e12 0.996927 -0.305964 0.995729
user_001 Jumping 1.44604723421e12 2.79798 -0.114362 0.919089
user_001 Jumping 1.446047234468e12 6.89825 -19.58108 -4.25415
user_001 Jumping 1.446047234663e12 0.805325 1.15021 -0.15388
user_001 Jumping 1.446047234986e12 5.36544 -4.09967 -0.537083
user_001 Jumping 1.446047235115e12 6.93658 -19.58108 -2.75966
user_001 Jumping 1.446047235633e12 1.18853 -2.72014 1.41725
user_001 Jumping 1.446047235763e12 6.55337 -19.58108 0.995729
user_001 Jumping 1.446047236281e12 3.56439 -5.90073 0.727487
user_001 Jumping 1.446047236346e12 7.05154 -17.97163 -1.61005
user_001 Jumping 1.446047236669e12 0.11556 -0.114362 -1.11189
user_001 Jumping 1.446047236798e12 1.80165 0.345482 0.765807
user_001 Jumping 1.446047236928e12 3.48775 -7.31858 -0.728685
user_001 Jumping 1.446047236992e12 5.32712 -18.92964 0.114362
user_001 Jumping 1.446047237057e12 10.34708 -19.58108 2.37526
user_001 Jumping 1.44604723764e12 4.98224 -19.27452 1.57053
user_001 Jumping 1.446047238287e12 6.32345 -17.62674 -0.728685
user_001 Jumping 1.446047238676e12 1.95493 0.230521 1.60885
user_001 Jumping 1.446047241849e12 -0.919089 2.6447 -3.14286
user_001 Jumping 1.446047243725e12 -1.07237 1.15021 0.152682
user_001 Jumping 1.446047243984e12 2.87462 -0.535886 -0.268841
user_001 Jumping 1.446047244243e12 14.86888 -19.58108 -1.41845
user_003 Jumping 1.446047778293e12 -3.63983 -9.04299 -2.6447
user_003 Jumping 1.446047780171e12 -3.37159 -5.78577 -3.87095
user_003 Jumping 1.446047780365e12 -3.86975 -19.58108 -5.40376
user_003 Jumping 1.446047781725e12 0.230521 0.268841 -0.345482
user_003 Jumping 1.446047782244e12 -1.45557 -7.85507 -1.22685
user_003 Jumping 1.446047782373e12 -1.64717 -5.90073 -2.33814
user_003 Jumping 1.446047783151e12 -1.14901 -14.98264 -4.13919
user_003 Jumping 1.446047783409e12 -3.67815 -4.44456 -2.4531
user_003 Jumping 1.446047784057e12 -2.8351 -17.62674 -5.02056
user_003 Jumping 1.44604778451e12 -0.344284 -6.51385 -2.8363
user_003 Jumping 1.446047784575e12 -4.02303 -14.98264 -2.2615
user_003 Jumping 1.446047784769e12 -2.4519 7.7239e-2 1.11069
user_003 Jumping 1.446047785158e12 -4.3296 -19.38948 -5.17384
user_003 Jumping 1.446047785287e12 -3.44823 -7.24194 -2.18486
user_003 Jumping 1.44604778587e12 -0.919089 -4.82776 -0.268841
user_003 Jumping 1.446047786129e12 -0.842448 -0.497565 -3.0279
user_003 Jumping 1.446047786453e12 -3.94639 -5.59417 -3.14286
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user_003 Sitting 1.446047539824e12 0.383802 -0.689167 9.50284
user_003 Sitting 1.446047540084e12 0.460442 -0.650847 9.38788
user_003 Sitting 1.446047540148e12 0.422122 -0.650847 9.4262
user_003 Sitting 1.446047540261e12 0.307161 -0.612526 9.46452
user_003 Sitting 1.446047540504e12 0.460442 -0.689167 9.4262
user_003 Sitting 1.446047540803e12 0.498763 -0.765807 9.6178
user_003 Sitting 1.446047540836e12 0.422122 -0.727487 9.2346
user_003 Sitting 1.446047540852e12 0.537083 -0.650847 9.31124
user_003 Sitting 1.446047540884e12 0.498763 -0.650847 9.46452
user_003 Sitting 1.446047540941e12 0.498763 -0.957409 9.54116
user_003 Sitting 1.446047541046e12 0.345482 -0.689167 9.4262
user_003 Sitting 1.44604754111e12 0.575403 -0.535886 9.50284
user_003 Sitting 1.446047541135e12 0.460442 -0.650847 9.38788
user_003 Sitting 1.4460475412e12 0.498763 -0.689167 9.27292
user_003 Sitting 1.446047541223e12 0.307161 -0.689167 9.46452
user_003 Sitting 1.44604754209e12 0.460442 -0.650847 9.46452
user_006 Sitting 1.446035991462e12 -0.229323 -1.26397 9.31124
user_006 Sitting 1.44603599224e12 -0.267643 -1.30229 9.34956
user_006 Sitting 1.446035992757e12 -0.191003 -1.14901 9.38788
user_006 Sitting 1.446035993534e12 -0.305964 -1.14901 9.38788
user_006 Sitting 1.446035993664e12 -0.305964 -1.22565 9.38788
user_006 Sitting 1.446035994765e12 -0.229323 -1.22565 9.4262
user_006 Sitting 1.446035995413e12 -0.229323 -1.22565 9.38788
user_006 Sitting 1.44603599606e12 -0.229323 -1.22565 9.4262
user_006 Sitting 1.446035996578e12 -0.229323 -1.22565 9.38788
user_006 Sitting 1.446035997161e12 -0.191003 -1.22565 9.4262
user_006 Sitting 1.446035998003e12 -0.267643 -1.22565 9.38788
user_006 Sitting 1.446035998391e12 -0.229323 -1.18733 9.38788
user_006 Sitting 1.446035999492e12 -0.229323 -1.18733 9.34956
user_006 Sitting 1.446036000334e12 -0.191003 -1.18733 9.38788
user_006 Sitting 1.446036000593e12 -0.229323 -1.22565 9.34956
user_006 Sitting 1.446036001046e12 -0.229323 -1.14901 9.38788
user_006 Sitting 1.446036001176e12 -0.229323 -1.18733 9.4262
user_006 Sitting 1.446036001241e12 -0.267643 -1.22565 9.38788
user_006 Sitting 1.446036001694e12 -0.152682 -1.18733 9.4262
user_006 Sitting 1.44603600273e12 -0.229323 -1.26397 9.38788
user_006 Sitting 1.446036003054e12 -0.229323 -1.18733 9.38788
user_006 Sitting 1.446036003184e12 -0.191003 -1.18733 9.4262
user_006 Sitting 1.446036003378e12 -0.229323 -1.22565 9.38788
user_006 Sitting 1.446036003443e12 -0.305964 -1.14901 9.38788
user_006 Sitting 1.446036003572e12 -0.191003 -1.26397 9.38788
user_006 Sitting 1.446036003702e12 -0.305964 -1.18733 9.38788
user_006 Sitting 1.446036004091e12 -0.229323 -1.22565 9.34956
user_006 Sitting 1.446036004285e12 -0.229323 -1.22565 9.38788
user_006 Sitting 1.446036004738e12 -0.229323 -1.11069 9.38788
user_006 Sitting 1.446036004997e12 -0.229323 -1.22565 9.38788
user_006 Sitting 1.446036005774e12 -0.305964 -1.18733 9.38788
user_006 Sitting 1.446036006875e12 -0.152682 -1.18733 9.34956
user_006 Sitting 1.446036006939e12 -0.267643 -1.30229 9.31124
user_006 Sitting 1.446036007653e12 -0.267643 -1.22565 9.38788
user_006 Sitting 1.446036007976e12 -0.229323 -1.22565 9.34956
user_007 Jogging 1.446309659174e12 -12.60678 -8.85139 2.22198
user_007 Jogging 1.446309659693e12 3.83263 -1.26397 1.83878
user_007 Jogging 1.446309661117e12 -15.90233 -10.03932 -2.68302
user_007 Jogging 1.446309661377e12 -7.51018 -6.74378 -1.83997
user_007 Jogging 1.446309661442e12 -15.0976 -12.30022 1.45557
user_007 Jogging 1.446309661635e12 -0.344284 -2.10702 -0.460442
user_007 Jogging 1.446309664809e12 -3.56319 -3.98471 1.03405
user_007 Jogging 1.446309665263e12 -16.17057 -11.22725 -2.91294
user_007 Jogging 1.446309665651e12 -2.98839 -6.39889 -0.11556
user_007 Jogging 1.446309666687e12 -3.94639 0.613724 0.229323
user_007 Jogging 1.446309667399e12 -14.86768 -3.71647 -5.82529
user_007 Jogging 1.446309671028e12 -5.13432 -3.29495 0.382604
user_007 Jogging 1.446309673683e12 -7.12698 -11.64878 -2.98958
user_007 Jogging 1.446309673943e12 -1.64717 0.843646 -1.87829
user_007 Jogging 1.446309674397e12 -1.60885 -0.919089 -1.18853
user_007 Jogging 1.446309674979e12 -13.90968 -7.81675 -0.843646
user_007 Jogging 1.446309675691e12 -11.57214 -12.0703 -2.49142
user_007 Jogging 1.446309675886e12 -3.79311 -7.51018 -1.49509
user_004 Walking 1.44604636711e12 2.2615 -7.05034 -0.728685
user_004 Walking 1.446046367952e12 3.2195 -10.23092 -0.422122
user_004 Walking 1.446046368146e12 -6.82042 -14.59944 0.574206
user_004 Walking 1.446046368276e12 0.958607 -6.7821 1.03405
user_004 Walking 1.44604636957e12 3.2195 -12.4535 -7.7413
user_004 Walking 1.446046369635e12 0.843646 -10.42253 -7.58802
user_004 Walking 1.446046370153e12 4.10087 -10.1926 -0.307161
user_004 Walking 1.446046370347e12 -7.43354 -15.97897 0.152682
user_004 Walking 1.446046371124e12 0.230521 -9.08132 7.6042e-2
user_004 Walking 1.446046371189e12 -0.727487 -8.96635 1.14901
user_004 Walking 1.446046372095e12 -1.53221 -13.18159 5.51753
user_004 Walking 1.446046373391e12 -1.68549 -8.16163 1.72382
user_004 Walking 1.446046374038e12 -1.34061 -1.99206 -2.95126
user_004 Walking 1.446046376822e12 0.15388 -9.54116 -7.7239e-2
user_004 Walking 1.446046377082e12 1.41845 -13.25823 0.842448
user_004 Walking 1.44604637883e12 -3.44823 -2.2603 1.22565
user_004 Walking 1.446046379219e12 2.49142 -10.72909 -1.07357
user_004 Walking 1.446046379801e12 1.80165 -10.1926 -7.43474
user_004 Walking 1.446046379995e12 -4.98104 -6.59049 3.21831
user_004 Walking 1.446046380349e12 0.881966 -11.34221 -1.30349
user_004 Walking 1.446046380357e12 -0.229323 -11.03565 -1.91661
user_004 Walking 1.446046380616e12 2.60638 -8.65979 -1.18853
user_004 Walking 1.446046380632e12 1.91661 -9.04299 -1.95493
user_004 Walking 1.446046380802e12 -0.152682 -3.71647 -4.86728
user_004 Walking 1.446046380818e12 0.422122 -5.01936 -6.47673
user_004 Walking 1.446046380826e12 0.920286 -5.67081 -7.12818
user_004 Walking 1.446046380891e12 3.18118 -18.96796 -9.92556
user_004 Walking 1.446046380899e12 2.22318 -16.28553 -8.58435
user_004 Walking 1.446046381053e12 -3.52487 -1.76214 0.650847
user_004 Walking 1.446046381109e12 -2.6435 -10.26924 2.10702
user_004 Walking 1.446046381166e12 4.63736 -9.92436 -3.29615
user_004 Walking 1.446046381377e12 -1.26397 -8.96635 -0.307161
user_004 Walking 1.446046381393e12 -0.497565 -9.27292 -1.68669
user_004 Walking 1.4460463814e12 0.307161 -9.34956 -1.38013
user_004 Walking 1.446046381539e12 0.767005 -13.10495 1.45557
user_004 Walking 1.446046381708e12 3.41111 -8.62147 -2.49142
user_004 Walking 1.446046381757e12 0.958607 -7.58682 -0.307161
user_004 Walking 1.446046381773e12 -0.152682 -6.51385 7.6042e-2
user_004 Walking 1.446046381797e12 -1.72382 -4.7128 1.22565
user_004 Walking 1.446046381822e12 -3.06503 -2.98839 0.727487
user_004 Walking 1.446046381854e12 -3.29495 -2.03038 -1.41845
user_004 Walking 1.446046381886e12 -1.14901 -2.22198 -4.02423
user_004 Walking 1.446046381919e12 0.958607 -6.16897 -5.40376
user_004 Walking 1.446046381959e12 3.71767 -15.05928 -7.70298
user_004 Walking 1.446046382089e12 -3.71647 -10.72909 -2.14654
user_004 Walking 1.446046382129e12 -11.76374 -19.50444 -0.843646
user_004 Walking 1.44604638221e12 -4.09967 -3.40991 5.096
user_004 Walking 1.446046382259e12 0.652044 -19.58108 4.63616
user_004 Walking 1.446046382316e12 -0.229323 -4.82776 -0.575403
user_004 Walking 1.446046382332e12 -3.86975 -4.48288 2.14534
user_004 Walking 1.446046382526e12 1.49509 -9.77108 -0.537083
user_004 Walking 1.446046382566e12 3.2195 -10.95901 -7.7239e-2
user_004 Walking 1.446046382647e12 -0.152682 -13.79471 0.574206
user_004 Walking 1.44604638285e12 2.60638 -7.77842 -2.33814
user_004 Walking 1.446046383157e12 -0.344284 -11.61046 -6.82161
user_004 Walking 1.446046383174e12 -1.22565 -8.08499 -7.08986
user_004 Jumping 1.446046440495e12 -0.612526 -0.995729 -0.728685
user_004 Jumping 1.446046440559e12 1.41845 -0.880768 -7.7239e-2
user_004 Jumping 1.44604644069e12 8.35443 -7.66346 -0.460442
user_004 Jumping 1.446046441337e12 14.02583 -14.7144 -0.958607
user_004 Jumping 1.446046442243e12 0.575403 1.15021 -0.690364
user_004 Jumping 1.446046442502e12 6.89825 -11.11229 -1.57173
user_004 Jumping 1.446046443085e12 3.94759 -0.305964 1.30229
user_004 Jumping 1.446046444705e12 -1.72382 1.26517 -1.45677
user_004 Jumping 1.446046444899e12 0.498763 1.30349 1.18733
user_004 Jumping 1.446046445028e12 19.19908 -19.08292 -3.79431
user_004 Jumping 1.446046445093e12 3.18118 -8.46819 1.30229
user_004 Jumping 1.446046445352e12 1.61005 -0.689167 -0.613724
user_004 Jumping 1.446046445482e12 12.03318 -8.88971 -0.920286
user_004 Jumping 1.446046446387e12 1.30349 -1.57053 -1.76333
user_004 Jumping 1.446046446777e12 1.15021 6.40009 -2.41478
user_004 Jumping 1.446046446842e12 -1.41725 0.230521 -1.30349
user_004 Jumping 1.446046447036e12 0.498763 0.690364 -1.34181
user_004 Jumping 1.44604644723e12 -1.26397 -3.60151 4.40624
user_004 Jumping 1.446046448784e12 16.36337 -16.86034 -1.72501
user_004 Jumping 1.446046448849e12 -0.535886 -3.83143 4.63616
user_004 Jumping 1.446046449172e12 1.38013 2.56806 0.191003
user_004 Jumping 1.446046449949e12 -2.0687 0.575403 1.07237
user_004 Jumping 1.446046450079e12 -1.76214 -0.459245 -0.728685
user_004 Jumping 1.446046450273e12 1.03525 0.996927 -2.33814
user_004 Jumping 1.446046450726e12 0.805325 -3.7722e-2 -0.767005
user_004 Jumping 1.446046451115e12 0.268841 4.40743 -3.06622
user_004 Jumping 1.446046451503e12 17.62794 -14.63776 -3.41111
user_004 Jumping 1.446046451697e12 -1.80046 1.76333 -2.10822
user_004 Jumping 1.446046452086e12 3.37279 -10.34589 3.14167
user_004 Jumping 1.446046452409e12 0.690364 1.49509 -1.68669
user_004 Jumping 1.446046453575e12 -0.305964 0.690364 -1.07357
user_004 Jumping 1.446046455194e12 0.881966 1.87829 -0.575403

Feature Selection on Running Windows

  1. ETL Using SparkSQL Windows

A Markov Process Assumption

This is sensible since the subjects are not instantaneously changing between the activities of interest: sitting, walking, jogging, etc. Thus it makes sense to try and use the most recent accelerometer readings (from the immediate past) to predict the current activity.

 // Import the window functions.
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

// Create a window specification
val windowSize = 10
val wSpec1 = Window.partitionBy("user_id","activity").orderBy("timeStampAsLong").rowsBetween(-windowSize, 0)
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
windowSize: Int = 10
wSpec1: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@ade1233
 // Calculate the moving window statistics from data
val dataFeatDF = dataDF
      .withColumn( "meanX", mean($"x").over(wSpec1)  )
      .withColumn( "meanY", mean($"y").over(wSpec1)  )
      .withColumn( "meanZ", mean($"z").over(wSpec1)  ) 
//resultant = 1/n * ∑ √(x² + y² + z²)
      .withColumn( "SqX", pow($"x",2.0) )
      .withColumn( "SqY", pow($"y",2.0) )
      .withColumn( "SqZ", pow($"z",2.0) )
      .withColumn( "resultant", pow( $"SqX"+$"SqY"+$"SqZ",0.50 ) )
      .withColumn( "meanResultant", mean("resultant").over(wSpec1) )
// (1 / n ) * ∑ |b - mean_b|, for b in {x,y,z} 
      .withColumn( "absDevFromMeanX", abs($"x" - $"meanX") )
      .withColumn( "absDevFromMeanY", abs($"y" - $"meanY") )
      .withColumn( "absDevFromMeanZ", abs($"z" - $"meanZ") )
      .withColumn( "meanAbsDevFromMeanX", mean("absDevFromMeanX").over(wSpec1) )
      .withColumn( "meanAbsDevFromMeanY", mean("absDevFromMeanY").over(wSpec1) )
      .withColumn( "meanAbsDevFromMeanZ", mean("absDevFromMeanZ").over(wSpec1) )
//standard deviation  = √ variance = √ 1/n * ∑ (x - u)² with u = mean x
      .withColumn( "sqrDevFromMeanX", pow($"absDevFromMeanX",2.0) )
      .withColumn( "sqrDevFromMeanY", pow($"absDevFromMeanY",2.0) )
      .withColumn( "sqrDevFromMeanZ", pow($"absDevFromMeanZ",2.0) )
      .withColumn( "varianceX", mean("sqrDevFromMeanX").over(wSpec1) )
      .withColumn( "varianceY", mean("sqrDevFromMeanY").over(wSpec1) )
      .withColumn( "varianceZ", mean("sqrDevFromMeanZ").over(wSpec1) )
      .withColumn( "stddevX", pow($"varianceX",0.50) )
      .withColumn( "stddevY", pow($"varianceY",0.50) )
      .withColumn( "stddevZ", pow($"varianceZ",0.50) )
dataFeatDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 27 more fields]
display(dataFeatDF.sample(false,0.1))
user_id activity timeStampAsLong x y z meanX meanY meanZ SqX SqY SqZ resultant meanResultant absDevFromMeanX absDevFromMeanY absDevFromMeanZ meanAbsDevFromMeanX meanAbsDevFromMeanY meanAbsDevFromMeanZ sqrDevFromMeanX sqrDevFromMeanY sqrDevFromMeanZ varianceX varianceY varianceZ stddevX stddevY stddevZ
user_001 Jogging 1.446047140892e12 -5.01936 0.383802 -1.61005 3.0104863636363643 -8.294006090909091 0.4139589090909091 25.193974809599997 0.147303975204 2.5922610025 5.285218991423534 12.114445037568451 8.029846363636365 8.67780809090909 2.024008909090909 5.574814628099175 8.743996363636365 1.8780127768595039 64.47843262360415 75.30435326264728 4.0966120640793715 39.26105423020038 86.44128603240851 4.762676135262259 6.265864204577081 9.297380600599746 2.1823556390428807
user_001 Jogging 1.446047141346e12 0.498763 -7.51018 1.83878 2.8084320909090907 -6.935377181818183 -4.936863636363633e-2 0.24876453016900002 56.4028036324 3.3811118884000004 7.748075893469875 10.526229014345958 2.309669090909091 0.5748028181818174 1.8881486363636364 4.870479504132231 7.062020214876034 2.769514024793388 5.334571309500826 0.3303982797897595 3.5651052730018598 28.32052218489271 67.50085446063852 9.63958031560342 5.3217029403089295 8.215890363231397 3.104767352895128
user_001 Jogging 1.44604714141e12 11.87989 -19.58108 2.75846 3.738570272727273 -7.440509 -7.027136363636353e-2 141.13178641209998 383.4186939664 7.609101571599999 23.06858430745372 11.31119913133375 8.141319727272727 12.140571000000001 2.8287313636363636 5.357875181818181 7.761603768595041 2.783764652892562 66.28108690168007 147.39346420604105 8.00172112762004 33.64351026346047 79.10395343437911 9.717968828501292 5.800302601025266 8.894040332401191 3.1173656873233995
user_001 Jogging 1.446047142255e12 5.32712 -18.35483 3.94639 3.254341727272727 -8.088470909090908 0.69265 28.378207494399998 336.8997843289 15.5739940321 19.515429430463477 14.672311468733064 2.0727782727272728 10.266359090909091 3.25374 4.968022991735537 9.121183421487604 2.997534545454546 4.296409767890256 105.39812898349174 10.5868239876 42.373279917383705 108.65275154076608 11.80757071836034 6.509476163055189 10.423663057714695 3.4362145914305673
user_001 Jogging 1.44604714236e12 -3.90807 1.87829 -1.03525 -0.38260427272727265 -4.622226636363637 1.309258636363637 15.2730111249 3.5279733241 1.0717425625 4.457883692011267 6.235267429173871 3.5254657272727274 6.500516636363637 2.3445086363636367 2.0246421404958674 4.5952694710743796 1.275971702479339 12.42890859417462 42.25671653964041 5.496720745983679 5.322841770497276 27.27538573170004 2.7565803183488558 2.3071284685724107 5.222584200537129 1.660295250354242
user_001 Jogging 1.446047142611e12 3.14286 -19.58108 11.57214 4.797603636363636 -11.600005181818183 3.4970007272727273 9.877568979600001 383.4186939664 133.9144241796 22.961068945621847 17.859074479791374 1.6547436363636359 7.981074818181817 8.075139272727272 4.461625685950413 7.032252909090909 8.588184256198346 2.7381765020859485 63.69755525341592 65.20787427394234 29.39875554713526 63.52664671148329 111.91945306916881 5.422061927637424 7.970360513269352 10.579199075032514
user_001 Jogging 1.446047142723e12 3.33447 -16.4005 4.59784 8.695826363636364 -19.19787727272727 1.5635675454545455 11.1186901809 268.97640025000004 21.140132665599996 17.356129265953857 21.88260842177062 5.361356363636364 2.7973772727272674 3.0342724545454542 5.770848892561983 0.974475619834712 4.766286942148761 28.74414205790414 7.825319605971044 9.206809328413296 47.590515226108074 2.4736297184957947 29.04037921776094 6.898587915371382 1.5727777079090977 5.3889126192359935
user_001 Jogging 1.446047143007e12 13.94919 -19.58108 -1.99325 7.406869090909091 -15.54699727272727 -2.132600909090909 194.5799016561 383.4186939664 3.9730455625 24.124088401118915 17.659646578855703 6.5423209090909085 4.034082727272731 0.13935090909090886 4.869846033057851 6.8751705371900815 2.2795826446280993 42.80196287752809 16.273823450480194 1.9418675864462744e-2 25.026920417585174 50.28981774904209 8.028885422595941 5.002691317439561 7.091531410706865 2.8335287933239606
user_001 Jogging 1.446047143412e12 -3.7722e-2 -19.38948 16.74538 2.933844363636364 -10.001001818181818 4.7859561818181815 1.422949284e-3 375.95193467039996 280.4077513444 25.61954544803799 14.94696596077227 2.971566363636364 9.388478181818181 11.959423818181818 4.184833462809917 6.708588429752067 8.137841272727274 8.830206653495043 88.14352257047602 143.0278180628946 25.580893194814248 48.75439847000317 93.00363187197318 5.057755746851981 6.9824349957592275 9.643839062944444
user_001 Jogging 1.446047143428e12 5.99e-4 -19.58108 2.49022 2.0455097272727274 -12.96560090909091 4.8382107272727275 3.5880100000000004e-7 383.4186939664 6.2011956484 19.738791502359028 17.17647355875381 2.0449107272727276 6.615479090909091 2.3479907272727276 3.7078880247933883 7.136446049586777 7.307778793388429 4.181659882515076 43.764563602255365 5.513060455358713 22.443816935824188 54.075963421190316 85.24184299019907 4.737490573692384 7.3536360680407835 9.232650918896429
user_001 Jogging 1.4460471435e12 8.92923 -19.58108 7.62514 0.5335997272727272 -19.581079999999996 1.8318090909090905 79.73114839290001 383.4186939664 58.1427600196 22.831833092831157 21.106302639228264 8.395630272727272 3.552713678800501e-15 5.793330909090909 4.901198421487603 2.9142437190082675 3.942557785123967 70.48660767633461 1.2621774483536189e-29 33.5626830222281 33.313236846605356 15.557764988992716 30.291063062675242 5.771762022693361 3.944333275598389 5.503731739708544
user_001 Jogging 1.446047143517e12 10.42372 -19.58108 8.73643 2.205758909090909 -19.581079999999996 2.4379672727272723 108.65393863839999 383.4186939664 76.3252091449 23.841095649103462 21.67139468669904 8.21796109090909 3.552713678800501e-15 6.298462727272728 6.114780479338842 1.5831671074380202 4.6259896033057855 67.53488449169573 1.2621774483536189e-29 39.67063272684381 45.04989788390212 5.722576186684003 36.09638973021352 6.711922070756045 2.3921906668750306 6.008027107979251
user_001 Jogging 1.446047143581e12 14.17911 -4.75112 -8.58435 12.837903636363638 -14.822395454545456 1.539181818181818 201.04716039209998 22.573141254400003 73.6910649225 17.24271923360698 21.99929601707681 1.3412063636363616 10.071275454545455 10.123531818181819 6.092612942148761 3.6242771074380187 7.671663884297521 1.7988345098586722 101.43058928132977 102.48589647373969 41.440687896949285 28.9054852091499 72.55417043068063 6.437444205346504 5.376382167326827 8.517873586211563
user_001 Jogging 1.446047143606e12 9.23579 -0.497565 0.919089 13.25594272727273 -10.042807727272727 -1.2721355454545458 85.2998169241 0.24757092922499999 0.8447245899210001 9.294735738214724 18.876966595567662 4.02015272727273 9.545242727272727 2.1912245454545456 4.424255107438015 6.262994958677688 6.90810826446281 16.161627950598373 91.11165872255289 4.80146500860248 24.680606942273855 54.43818419281497 66.47172910922194 4.967958025413847 7.378223647519434 8.153019631352665
user_001 Jogging 1.44604714363e12 5.82529 3.14286 3.21831 11.935633636363635 -4.315663545454545 -2.742242818181819 33.9340035841 9.877568979600001 10.357519256099998 7.359965476807618 14.48403310791005 6.110343636363635 7.458523545454545 5.960552818181819 4.2234690247933875 8.366494983471073 7.1579815123966934 37.33629935444957 55.629573478099836 35.52818989833523 22.091004368014264 72.82379547301899 68.08732499721447 4.700106846446607 8.53368592537943 8.251504408119436
user_001 Jogging 1.446047143687e12 0.805325 5.40376 -1.18853 4.815022272727273 3.9789428181818183 1.4346706363636363 0.6485483556249999 29.2006221376 1.4126035609000003 5.591222947989554 7.279610887559767 4.009697272727273 1.4248171818181818 2.623200636363636 4.808407685950414 5.901961702479338 3.262293380165289 16.07767221891653 2.0301040016043057 6.881181578618586 23.86087788087626 40.88601350062889 14.184341683283828 4.8847597567205145 6.394217192168944 3.7662105203086864
user_001 Jogging 1.446047143703e12 0.498763 3.67935 -5.0972 3.3832365454545457 4.700061818181818 0.36518618181818213 0.24876453016900002 13.5376164225 25.98144784 6.306173863181145 6.765815236942559 2.884473545454546 1.0207118181818182 5.462386181818182 4.530347388429752 4.320377892561984 3.515650859504132 8.320187634427118 1.041852615776033 29.83766279931822 21.64134645781683 25.2878319216034 16.07774203478182 4.652026059451606 5.028700818462299 4.009705978595166
user_001 Jogging 1.4460471438e12 6.36177 -19.58108 6.55217 2.181372181818182 -11.164546363636363 3.423636363636378e-2 40.4721175329 383.4186939664 42.930931708900005 21.60605802103197 12.990120579315441 4.180397818181818 8.416533636363637 6.517933636363637 2.872756842975207 9.207959504132232 3.702816834710744 17.475725918259307 70.83803845204051 42.4834588880405 10.08973643029246 92.24096030173884 21.23336541278622 3.1764345468295834 9.604215756725733 4.607967601099884
user_001 Jogging 1.446047143865e12 7.66466 -19.58108 2.37526 6.337383636363636 -19.581079999999996 3.5875754545454543 58.747012915599996 383.4186939664 5.6418600676 21.1614641967327 21.061833583938864 1.3272763636363631 3.552713678800501e-15 1.2123154545454544 2.492719975206612 4.63169049586777 2.403411570247934 1.7616625454677672 1.2621774483536189e-29 1.4697087613297517 7.974185257215893 38.68784266457513 9.625501871091812 2.82385999249536 6.21995519795562 3.1024992942935237
user_001 Jogging 1.446047143906e12 2.22318 -17.58843 3.67815 5.58143 -19.372060909090905 3.406424545454546 4.9425293124000005 309.35286986489996 13.5287874225 18.10591579014439 20.5777606506816 3.35825 1.7836309090909062 0.27172545454545416 1.7852188099173552 0.911135950413226 1.229100991735537 11.2778430625 3.1813392198644523 7.383472264793367e-2 4.562496695217112 2.185291967910373 2.2448866682423736 2.136000162738082 1.4782733062293905 1.4982945866025057
user_001 Jogging 1.446047143914e12 2.03158 -15.25089 2.91174 5.28531909090909 -18.97840727272727 3.3541690909090907 4.127317296399999 232.58964579210001 8.4782298276 15.658709810073754 20.13034914244241 3.2537390909090904 3.7275172727272707 0.44242909090909066 1.9736531404958675 0.9219033057851264 1.1711456198347105 10.586818071709914 13.89438501848015 0.1957435004826444 5.398146523816002 2.2642876788123227 2.1566573067818178 2.32339116892012 1.5047550228566517 1.468556198033231
user_001 Jogging 1.446047143946e12 0.805325 -4.67448 1.80046 3.285695 -15.020963636363634 2.7131754545454543 0.6485483556249999 21.850763270399998 3.2416562115999996 5.073555739087233 15.738688413790378 2.48037 10.346483636363633 0.9127154545454543 2.33848826446281 3.4744802479338843 0.8097950413223138 6.152235336900001 107.04972363754042 0.8330495009661153 7.003361392995342 26.536731773096072 0.9551943565163034 2.6463864783880946 5.15138154023715 0.9773404506702377
user_001 Jogging 1.446047144043e12 -4.78944 6.93658 0.114362 -4.071804181818182 5.051913545454545 -2.2614949999999996 22.938735513599998 48.116142096400004 1.3078667044000002e-2 8.430181271897064 7.023773744750437 0.7176358181818179 1.8846664545454548 2.3758569999999994 2.8974588016528924 7.285610578512396 2.8448858760330578 0.5150011675374871 3.5519676448889346 5.644696484448997 9.729573142987029 65.17831055224397 9.956481900398632 3.119226369308106 8.073308525768352 3.1553893421254107
user_001 Jogging 1.446047144092e12 -1.99206 4.21583 -2.98958 -4.1693490909090904 6.340868181818181 -2.1778882727272726 3.9683030435999997 17.773222588900005 8.937588576400001 5.538873008916164 8.085371578268587 2.1772890909090905 2.125038181818181 0.8116917272727275 1.5590985867768594 2.253931280991736 1.5172942727272727 4.7405877853917335 4.51578727418512 0.6588434601229839 3.3676530779136438 8.457330240795045 3.1033527599789106 1.8351166387763052 2.9081489371755094 1.761633548720877
user_001 Jogging 1.446047144115e12 3.60271 3.8919e-2 -6.36177 -2.4414504545454547 4.870761454545455 -2.3311700909090907 12.9795193441 1.514688561e-3 40.4721175329 7.3111662247250955 6.858484763711422 6.044160454545455 4.8318424545454555 4.03059990909091 1.9356506115702479 2.061379578512397 1.4881586776859501 36.53187560029112 23.346701505547852 16.24573562716365 6.431645076172794 6.610973877450948 3.3286675864357367 2.536068823232681 2.5711814166742393 1.824463643495188
user_001 Jogging 1.44604714414e12 6.28513 -1.72382 3.60151 0.6938477272727271 3.0348691818181806 -2.150019636363636 39.5028591169 2.9715553923999996 12.970874280100002 7.446159331454035 6.6932802788723516 5.591282272727272 4.7586891818181805 5.751529636363636 3.6945847933884295 2.4996878512396696 2.011975041322314 31.262437453314252 22.645122729153385 33.08009315796922 20.548499768802852 9.118378197963445 7.077055657840811 4.533045308487756 3.0196652460104656 2.6602736058234333
user_001 Jogging 1.44604714418e12 -3.56319 -9.92436 -3.98591 2.9617137272727274 -2.009475363636364 0.6369127272727273 12.696322976100001 98.4929214096 15.8874785281 11.272831184480676 7.331966995972979 6.5249037272727275 7.914884636363636 4.622822727272728 4.575635462809917 4.63612326446281 4.079687826446281 42.57436865017753 62.64539880694513 21.370489967789258 26.438045152664895 23.685324490452658 22.525681280821853 5.141793962486721 4.866757081512561 4.74612276293206
user_001 Jogging 1.446047144221e12 4.714 -19.58108 7.6042e-2 3.884886 -8.196463636363637 3.967295636363637 22.221796000000005 383.4186939664 5.782385764e-3 20.140662162703688 14.018026468325372 0.8291140000000006 11.384616363636363 3.8912536363636367 3.830131181818182 6.218974305785125 8.42825211570248 0.6874300249960009 129.60948974717684 15.141854862513226 19.765861701657393 44.709337568159334 94.92463205415766 4.445881431353899 6.686504136554418 9.742927283632865
user_001 Jogging 1.446047144254e12 -1.18733 -19.58108 7.93171 2.818885454545455 -14.29636090909091 4.907885636363637 1.4097525289 383.4186939664 62.9120235241 21.159878780829533 19.09860199176969 4.006215454545455 5.284719090909091 3.023824363636363 3.438377512396694 7.292894024793389 7.597556247933884 16.049762268238844 27.928255869819008 9.143513782120856 17.21997488706634 58.23402061541601 88.07572005384371 4.1496957583739 7.6311218451428235 9.38486654427455
user_001 Jogging 1.446047144278e12 1.22685 -19.58108 5.44089 3.083644 -17.351535454545452 8.287040181818181 1.5051609225 383.4186939664 29.603283992099996 20.35993955985626 20.824887979127013 1.856794 2.229544545454548 2.8461501818181816 3.1305487933884297 6.410893917355373 5.5722790413223136 3.4476839584360004 4.970868880166127 8.100570857463667 14.594451388452198 49.33036109995855 50.57366699564773 3.8202684969059697 7.023557581451052 7.111516504631606
user_001 Jogging 1.446047144311e12 11.49669 -19.58108 7.39522 4.355181272727273 -19.535792727272725 6.182906363636363 132.1738809561 383.4186939664 54.6892788484 23.880574820780595 21.77720408013568 7.1415087272727265 4.528727272727551e-2 1.2123136363636373 4.527814231404959 3.726571074380167 2.767612314049586 51.00114690171252 2.050937071074632e-3 1.4697043529132254 29.305284421350223 23.940853207647194 8.62882105866129 5.413435546984024 4.892939117508739 2.937485499310812
user_001 Jogging 1.446047144521e12 2.95126 -1.11069 -7.47306 2.6133480000000002 2.1569868181818177 -3.048805 8.7099355876 1.2336322760999998 55.8466257636 8.111115436689333 5.523819861858547 0.33791199999999977 3.2676768181818177 4.4242550000000005 3.396256438016529 3.6597486033057858 3.397523859504133 0.11418451974399985 10.677711788082847 19.574032305025003 16.3348600923496 20.826787642321772 14.775003279205595 4.041640767355457 4.5636375450206135 3.8438266453113616
user_001 Jogging 1.44604714465e12 1.99325 -19.58108 4.25296 3.5539354545454547 -19.33373999999999 3.173021363636363 3.9730455625 383.4186939664 18.0876687616 20.136519269488954 20.04234972512039 1.5606854545454547 0.24734000000000833 1.0799386363636367 1.341211223140496 5.445601107438019 1.5103279338842974 2.4357390880297527 6.117707560000412e-2 1.1662674583109511 2.0902997020591165 46.50264074581899 3.8864219929065356 1.4457868798889817 6.819284474621878 1.9714010228531726
user_001 Jogging 1.446047144779e12 -0.191003 0.920286 -0.230521 2.95823 -5.57326509090909 0.24325772727272724 3.6482146009e-2 0.8469263217960001 5.3139931441e-2 0.9677543072732873 6.782599679498083 3.1492329999999997 6.49355109090909 0.47377872727272724 1.9058816776859502 7.382202495867769 1.6845096694214876 9.917668488288998 42.166205770246634 0.22446628241616526 4.348254975048362 56.287978164098156 4.396775017887605 2.0852469817861774 7.502531450390472 2.096848830480539
user_001 Jogging 1.446047144909e12 3.44943 2.8363 -4.59904 -1.8422599999999998 5.741678181818181 -3.3414341818181814 11.8985673249 8.04459769 21.151168921599997 6.410486248054823 8.646625001195995 5.29169 2.905378181818181 1.2576058181818182 1.700345950413223 2.538641636363636 4.040733280991735 28.0019830561 8.44122237938512 1.5815723939247603 5.881941722491503 7.297593555367826 31.585934417060354 2.4252714739780172 2.7014058479554355 5.620136512315368
user_001 Jogging 1.446047144965e12 -1.49389 -10.80573 -8.50771 1.8608763636363634 -1.807423636363636 -0.2583900000000001 2.2317073320999996 116.76380083290002 72.3811294441 13.833894520672768 10.624124936163746 3.3547663636363634 8.998306363636365 8.249319999999999 3.971061611570248 5.76831652892562 7.454093057851239 11.254457354585949 80.9695174138587 68.05128046239999 21.70051268411826 45.90407271894876 64.16546233071315 4.658380908010664 6.775254439425043 8.010334720266885
user_001 Jogging 1.446047145006e12 5.2888 -19.50444 1.26397 4.592069272727273 -9.882558181818181 4.744153636363636 27.97140544 380.42317971359995 1.5976201609 20.248264254362642 15.73332539025025 0.696730727272727 9.621881818181818 3.4801836363636363 3.560938619834711 8.04219826446281 9.370108743801651 0.4854337063259831 92.58060972305783 12.111678142813222 18.604081060610227 68.5146688204746 105.59536052408288 4.3132448412546935 8.277358807039514 10.275960321258685
user_001 Jogging 1.446047145063e12 3.83263 -19.58108 6.51385 2.3694895454545457 -17.786991818181818 6.966730909090909 14.6890527169 383.4186939664 42.430241822499994 20.988996843722663 20.711596889168277 1.4631404545454543 1.7940881818181822 0.4528809090909096 3.6613319669421487 5.671721735537191 3.724989256198347 2.1407799897274784 3.218752404139671 0.20510111781900875 18.391050523378283 38.52759670800473 38.85600186927499 4.288478812280443 6.207060230737634 6.233458259206922
user_001 Jogging 1.446047145095e12 8.39275 -19.58108 7.77842 3.1184786363636365 -19.581079999999996 5.907696363636364 70.4382525625 383.4186939664 60.50381769639999 22.679523015824206 21.447303073035563 5.2742713636363625 3.552713678800501e-15 1.8707236363636355 5.020279380165289 3.0450398347107455 1.37161479338843 27.817938417274576 1.2621774483536189e-29 3.4996069236495835 31.002898462194388 16.330388050525777 2.60981791889955 5.568024646335035 4.041087483651619 1.6154930884716128
user_001 Jogging 1.446047145298e12 2.4531 2.68302 -2.18486 2.9512618181818184 2.784047636363636 -1.7006264545454548 6.01769961 7.1985963204 4.7736132196000005 4.241451302325656 4.838002482542072 0.49816181818181837 0.10102763636363621 0.4842335454545452 2.036044016528926 4.023950479338843 1.7253638842975207 0.24816519709421506 1.0206583309223109e-2 0.2344821265434791 5.415028704769436 23.42669537789521 4.255417333194267 2.3270214233585036 4.84011315755068 2.062866290672827
user_001 Jogging 1.446047145826e12 3.18118 -19.58108 4.7128 5.462984545454545 -11.24467181818182 3.880204545454545 10.1199061924 383.4186939664 22.21048384 20.389926042013983 16.88118003999633 2.2818045454545453 8.33640818181818 0.8325954545454546 3.494114983471074 8.170460165289256 7.975376504132232 5.206631983657024 69.4957013738851 0.6932151909297521 16.238127510167725 74.18152678299337 85.41361229590531 4.0296560039496825 8.612869834323131 9.241948511861843
user_001 Jogging 1.446047145906e12 7.24314 -19.58108 1.57053 1.3661963636363637 -19.581079999999996 4.9705900000000005 52.4630770596 383.4186939664 2.4665644809 20.936769939675507 20.824778135048515 5.876943636363636 3.552713678800501e-15 3.4000600000000007 4.809673801652893 3.080192148760331 2.9028430578512396 34.53846650499504 1.2621774483536189e-29 11.560408003600005 26.078049228796736 17.058755092359807 9.280744192829529 5.10666713510845 4.130224581346614 3.0464313865290857
user_001 Jogging 1.446047145914e12 4.79064 -19.58108 2.52854 1.5125109090909092 -19.581079999999996 4.772020909090909 22.9502316096 383.4186939664 6.3935145316 20.316555813119503 20.818108114239923 3.2781290909090908 3.552713678800501e-15 2.2434809090909087 4.9002487603305775 2.3223368595041345 3.0311053719008267 10.746130336664462 1.2621774483536189e-29 5.03320658945537 26.581639988161047 10.74096405837025 9.675288865422766 5.155738549244041 3.277341004285372 3.1105126370781337
user_001 Jogging 1.446047145947e12 2.68302 -16.24721 0.267643 3.2264732727272727 -19.002790909090912 2.504155 7.1985963204 263.97183278409995 7.163277544900001e-2 16.46942809814442 19.77419717829964 0.5434532727272727 2.755580909090913 2.236512 2.9690325619834708 0.9991787603305805 2.7372102479338847 0.29534145963798347 7.593226146546302 5.001985926143999 13.834117142120611 2.145853908953498 7.944784113633844 3.719424302512502 1.4648733422905538 2.8186493420845813
user_001 Jogging 1.446047146142e12 2.49142 -2.22198 -3.41111 6.225908181818181 3.0348706363636366 -3.9092709090909086 6.207173616400001 4.937195120399999 11.635671432099999 4.772844033582074 8.453049060126027 3.734488181818181 5.2568506363636365 0.4981609090909087 2.676405371900827 3.922289024793389 1.6018522727272726 13.946401980139665 27.63447861303677 0.24816429134628062 8.224617051718033 20.548089963168977 5.414473050834811 2.867859315189299 4.533000106239683 2.3269020286283673
user_001 Jogging 1.446047146191e12 2.2615 -18.23987 1.8771 3.0209363636363644 -5.601133909090908 -1.3069736363636362 5.114382249999999 332.69285761689997 3.52350441 18.475138545540055 8.593185614974145 0.7594363636363646 12.638736090909092 3.184073636363636 2.3581242148760326 6.585709553719008 2.062647512396694 0.5767435904132245 159.73764997564803 10.13832492178595 6.783338460343121 54.960802016525165 6.4349872719740056 2.6044842983483547 7.413555288559273 2.5367276700454084
user_001 Jogging 1.446047146222e12 6.70665 -19.58108 1.34061 3.292661818181818 -13.000438181818181 0.8041272727272727 44.9791542225 383.4186939664 1.7972351721000002 20.741144697460648 13.747680096855284 3.4139881818181816 6.580641818181819 0.5364827272727274 1.9527515702479334 8.756033107438014 2.4198804049586773 11.655315305594213 43.30484673920332 0.2878137166619836 5.276590136867767 83.31892515202385 7.355384939688656 2.2970829625565914 9.127920089046784 2.7120812929719964
user_001 Jogging 1.446047146247e12 6.70665 -19.58108 -2.0699 4.581617272727272 -17.348051818181816 1.1106894545454544 44.9791542225 383.4186939664 4.28448601 20.801017624118778 18.08485443258767 2.1250327272727274 2.2330281818181845 3.1805894545454545 1.8029535537190085 8.180911776859503 2.438882479338843 4.515764091980166 4.986414860794227 10.116149278365752 4.334497354961382 78.27161570846312 7.517023131813722 2.081945569644265 8.847124714191787 2.7417190103680795
user_001 Jogging 1.446047146255e12 6.55337 -19.58108 -1.61005 4.947401818181818 -18.389667272727266 0.8738004545454544 42.9466583569 383.4186939664 2.5922610025 20.71129192797494 19.18895665104643 1.6059681818181826 1.1914127272727342 2.4838504545454545 1.8089707438016531 7.583304611570248 2.3552742644628095 2.579133801012399 1.4194642867074545 6.169513080545661 4.353425914253344 72.91914457913747 7.02478885722426 2.086486499897218 8.539270728764691 2.6504318246701346
user_001 Jogging 1.446047146271e12 5.74865 -19.58108 -0.307161 5.564010909090908 -19.452184545454543 0.41047318181818165 33.04697682249999 383.4186939664 9.434787992100001e-2 20.409802024243668 20.332815201913306 0.18463909090909159 0.12889545454545726 0.7176341818181817 1.7611500826446287 5.651453752066117 1.715229809917355 3.4091593891735786e-2 1.6614038202480037e-2 0.5149988189138511 4.313352366296018 51.02793158470397 3.854774185943344 2.076861181277174 7.143383762944839 1.9633578853442244
user_001 Jogging 1.446047146295e12 4.10087 -19.58108 -0.422122 5.940246363636363 -19.581079999999996 -7.566363636363579e-3 16.817134756899996 383.4186939664 0.178186982884 20.010347715774056 20.51464954273569 1.8393763636363634 3.552713678800501e-15 0.4145556363636364 1.8916290082644627 2.4962036611570273 1.1429585702479337 3.3833054071041313 1.2621774483536189e-29 0.17185637564085954 4.597386198170472 14.204368818450723 2.222369381335169 2.1441516266743994 3.7688683737231687 1.490761342849743
user_001 Jogging 1.44604714649e12 0.958607 3.52607 0.344284 -0.678715818181818 1.8713269090909093 2.333455818181818 0.918927380449 12.4331696449 0.11853147265599999 3.6702354826366386 3.7110363855085042 1.637322818181818 1.6547430909090906 1.989171818181818 1.7076289090909091 3.528951776859505 1.723146338842975 2.680826010938851 2.7381746969113707 3.95680452224876 3.4146557666225394 14.091008592012436 3.72855255493712 1.847878720755921 3.753799221057572 1.9309460258995124
user_001 Jogging 1.446047146627e12 8.77595 -15.13592 11.64878 3.8744349090909096 -4.806858181818182 -2.1465371818181818 77.0172984025 229.0960742464 135.69407548840002 21.01921616372266 10.213391746577853 4.90151509090909 10.329061818181819 13.795317181818183 3.0817764958677687 5.029777553719009 5.655888396694215 24.024850186409545 106.6895180438215 190.31077614696795 12.519304124182344 31.099654858346547 56.03006032522963 3.538262868157529 5.576706452588889 7.485322994048395
user_001 Jogging 1.4460471467e12 0.881966 -19.58108 -1.45677 3.3065973636363637 -18.302574545454544 6.210776818181818 0.7778640251560001 383.4186939664 2.1221788328999995 19.654992669152943 20.497246490507834 2.4246313636363634 1.2785054545454564 7.667546818181818 3.0073537190082646 6.956878148760333 7.472461669421488 5.8788372495291314 1.6345761973024842 58.79127420901012 11.837209034079082 60.051072653539926 68.10106476024005 3.440524528916933 7.7492627167711845 8.252336927212804
user_001 Jogging 1.446047146708e12 2.18486 -19.58108 -0.1922 2.616831909090909 -19.065497272727267 5.611586818181818 4.7736132196000005 383.4186939664 3.694084e-2 19.703533896892708 20.818008869068755 0.4319719090909091 0.5155827272727329 5.803786818181818 2.473402603305785 6.281047404958678 7.099076429752066 0.18659973024364462 0.26582554866198926 33.68394143090103 8.239763534897493 54.32996020046944 62.23338618681305 2.8704988303250523 7.370885984769364 7.888813991140433
user_001 Jogging 1.446047146789e12 5.94025 -12.68342 0.804128 5.170359090909091 -17.612809090909092 2.316038 35.2865700625 160.8691428964 0.646621840384 14.02862554918635 18.616420879792923 0.7698909090909085 4.929389090909092 1.5119099999999999 2.1500545950413223 1.5967857024793408 1.880862933884297 0.5927320119008256 24.298876809573567 2.2858718480999998 5.820509118572988 5.918452160384152 5.775440422698065 2.4125731322745407 2.4327869122436825 2.4032146018818348
user_001 Jogging 1.446047146846e12 14.94552 2.52974 -7.08986 8.124505454545455 -8.865327727272726 -0.9760245454545455 223.36856807040002 6.3995844675999995 50.2661148196 16.734224432509563 14.08539729512732 6.821014545454545 11.395067727272727 6.1138354545454545 2.543391082644628 6.365605413223141 2.754627909090909 46.52623942930248 129.84756850913243 37.37898396525703 11.835538075822928 47.25518283891866 14.352842783440092 3.4402816855343294 6.874240528154267 3.7885145879935704
user_001 Jogging 1.44604714691e12 2.6447 6.51505 -2.14654 8.817753636363635 3.034871363636364 -3.5852909090909093 6.994438089999999 42.44587650249999 4.607633971599999 7.351730991004771 10.905216461572586 6.173053636363635 3.4801786363636356 1.4387509090909094 4.208898595041322 7.25900756198347 2.4344469173553724 38.1065911974223 12.111643341001853 2.0700041784099183 22.049739906490682 58.57205085462563 13.497022673672406 4.69571505805992 7.653237932707021 3.673829429038916
user_001 Jogging 1.446047146926e12 0.881966 6.09353 -1.80165 6.790261454545456 4.644324545454546 -2.839787272727272 0.7778640251560001 37.1311078609 3.2459427224999997 6.4152096309127735 9.492024266035791 5.908295454545455 1.4492054545454547 1.0381372727272722 4.2548196694214875 5.969101487603305 1.4526874958677687 34.90795517820249 2.1001964494842977 1.0777289970256188 22.276377763682646 44.06520485639808 4.615243289794645 4.719785775189658 6.63816276212011 2.1483117301254593
user_001 Jogging 1.446047146991e12 5.71033 -7.7401 3.67815 1.2895554545454546 1.2895539999999999 0.4069892727272728 32.6078687089 59.90914801 13.5287874225 10.29785434648403 5.452298577041354 4.420774545454545 9.029654 3.2711607272727274 4.018882578512398 4.053401785123968 2.1415043057851237 19.543247581738843 81.53465135971601 10.70049250365144 19.656272774198364 24.692266006893522 5.525528248539532 4.433539531141948 4.969131313106298 2.3506442198979265
user_001 Jogging 1.446047147008e12 5.86361 -14.5228 1.64717 1.996739090909091 -2.2498487272727274 1.2430692727272727 34.3819222321 210.91171984000002 2.7131690089 15.748231998576857 6.790361057403035 3.8668709090909097 12.272951272727273 0.4041007272727273 3.7151708429752066 5.61915094214876 2.1373874958677685 14.952690627573558 150.62533294273797 0.16329739778234711 16.52413057886283 47.64363520050973 5.706252704011319 4.064988386067398 6.902436903044441 2.3887764031008256
user_001 Jogging 1.44604714755e12 16.86154 -19.58108 -1.49509 6.253777272727273 -19.581079999999996 3.8035627272727273 284.31153117160005 383.4186939664 2.2352941081 25.883692148650276 21.360767742613746 10.607762727272728 3.552713678800501e-15 5.298652727272727 3.1489165289256196 3.1394150413223163 2.8486857355371904 112.52463007811656 1.2621774483536189e-29 28.07572072423471 16.449431349785637 18.39730278650308 10.253797064157284 4.055789855229883 4.289207710813628 3.2021550656014903
user_001 Jogging 1.446047147622e12 2.37646 -6.36057 -5.13552 6.630013181818182 -14.672596363636364 0.23280781818181825 5.647562131599999 40.4568507249 26.373565670399998 8.513399939325064 17.26086851800695 4.253553181818182 8.312026363636363 5.368327818181818 5.596982917355372 3.5789906611570257 3.841849239669422 18.09271467055558 69.08978226978594 28.81894356346476 38.34505562899607 23.791265035335616 19.437297262372166 6.192338462083292 4.877629038306995 4.408775029684795
user_001 Jogging 1.446047147639e12 2.10822 -3.86975 -4.36911 4.651291363636364 -11.934435454545456 -0.6485594545454547 4.444591568400001 14.974965062499999 19.0891221921 6.205536143074182 14.146659379883403 2.543071363636364 8.064685454545456 3.7205505454545453 4.807773413223141 5.056698264462809 3.875102727272727 6.467211960547316 65.03915148075706 13.842496361282114 28.276395911400765 35.80188864520639 19.261341575418438 5.317555445070673 5.983467944696152 4.388774495849432
user_001 Jogging 1.446047147655e12 3.2195 -0.995729 -4.86728 2.839785 -8.691143545454544 -1.735463090909091 10.36518025 0.991476241441 23.6904145984 5.920056679613887 10.914676311816635 0.379715 7.695414545454544 3.131816909090909 3.883018702479339 6.473599669421486 4.033451586776859 0.14418348122500002 59.21940502639337 9.808277152067735 21.574299434066237 46.895448857992044 19.698109391720433 4.644814251836799 6.848025179421586 4.438255219308646
user_001 Jogging 1.446047148035e12 -2.52854 0.307161 1.80046 2.7596614545454545 -7.064272636363637 0.633429 6.3935145316 9.434787992100001e-2 3.2416562115999996 3.1192176299708554 8.707368733999472 5.2882014545454545 7.371433636363637 1.167031 4.633590611570247 7.0259181818181835 0.9637088925619838 27.965074623856662 54.338033855313235 1.361961354961 26.853786031159323 51.463071213056516 1.4078366659209935 5.1820638775645484 7.1737766352916585 1.1865229310556933
user_001 Jogging 1.446047148423e12 3.41111 -10.84405 -5.99e-4 4.522395454545454 -16.672220000000003 2.9640000909090904 11.635671432099999 117.59342040249999 3.5880100000000004e-7 11.367897439430081 17.70845344887635 1.1112854545454542 5.828170000000004 2.9645990909090902 2.8486870247933878 2.1940752892561997 1.3709815702479338 1.2349553614842967 33.96756554890004 8.788847769819004 9.0019158055592 10.105985401013 2.663159615259104 3.0003192839361614 3.1789912552589694 1.6319189977627884
user_001 Jogging 1.446047148585e12 3.10454 -5.24928 -0.230521 7.291908181818181 -0.17706772727272738 -1.0526655454545453 9.638168611600001 27.5549405184 5.3139931441e-2 6.102970511270803 8.810267540587791 4.187368181818181 5.072212272727272 0.8221445454545453 2.3407064462809912 2.738793338842975 5.057014528925619 17.5340522901033 25.72733733960516 0.6759216536206609 8.01479266604553 10.180405784232084 31.71471424852974 2.8310409156431366 3.1906748164349317 5.631581860235163
user_001 Jogging 1.446047148658e12 8.85259 -19.58108 -0.996927 5.82877 -13.097980909090909 3.5980274545454547 78.36834970809998 383.4186939664 0.993863443329 21.512343134066754 15.243843861466813 3.0238199999999997 6.483099090909091 4.594954454545455 1.8757956198347103 7.861681504132232 4.55061688429752 9.143487392399999 42.03057382254628 21.11360643934712 5.88666845142667 68.0709621098762 23.399370359750957 2.426245752479882 8.250512839204372 4.837289567490348
user_001 Jogging 1.446047148723e12 11.57333 -19.58108 4.5212 11.754482727272727 -19.581079999999996 3.4377791818181818 133.9419672889 383.4186939664 20.441249440000004 23.19055649818046 23.48316918410363 0.18115272727272647 3.552713678800501e-15 1.0834208181818186 3.3810542975206612 3.4684628016528944 2.8087840661157024 3.281631059834682e-2 1.2621774483536189e-29 1.1738006692697611 15.367657156510823 23.07182841418071 12.982727905804962 3.9201603483162297 4.803314315572187 3.603155270843176
user_001 Jogging 1.446047148933e12 -6.55217 0.958607 0.305964 -8.673726363636364 1.5926342727272726 -1.1432400909090907 42.930931708900005 0.918927380449 9.361396929600001e-2 6.628987332816756 10.970671818551988 2.1215563636363637 0.6340272727272727 1.4492040909090909 6.460932644628099 4.056886380165289 2.3039676280991737 4.501001404085951 0.4019905825619834 2.1001924971076447 69.60176164731864 24.599329097919824 6.676603281846297 8.342767025832535 4.959771073136322 2.5839123982531405
user_001 Jogging 1.446047148956e12 0.805325 0.996927 -7.81794 -5.966918636363638 1.8991967272727273 -2.0733782727272723 0.6485483556249999 0.993863443329 61.120185843600005 7.92228487511993 9.345324426929823 6.772243636363638 0.9022697272727273 5.744561727272728 4.943003884297521 2.234929743801653 2.3356376694214878 45.863283870267786 0.8140906607528016 32.99998943844663 48.60011409208167 10.094791644327438 7.651563012286325 6.971378206070996 3.177230184347278 2.7661458768991785
user_001 Jogging 1.446047148981e12 12.79958 -0.650847 -9.69564 5.633772727272737e-2 1.442836727272727 -4.118291 163.82924817640003 0.42360181740899994 94.00543500959998 16.07041645395069 9.496347796961764 12.743242272727274 2.093683727272727 5.577348999999999 6.552456983471075 1.3722480165289257 3.4896825041322317 162.3902236214234 4.383511549846618 31.10682186780099 75.01246971118104 4.620914972535286 18.221982671141372 8.660973947032806 2.1496313573576487 4.268721432834587
user_001 Jogging 1.446047149102e12 3.90927 -19.58108 6.24561 6.884320090909092 -13.331385454545455 5.482689090909091 15.282391932899998 383.4186939664 39.0076442721 20.921489673811468 20.6720219868055 2.975050090909092 6.249694545454545 0.7629209090909095 3.4754289752066114 6.580958413223141 10.052906661157023 8.850923043418195 39.0586819114843 0.5820483135280998 18.677426649414937 52.092639445531944 133.3166367591787 4.321738845582289 7.217523082438459 11.546282378288636
user_001 Jogging 1.446047149231e12 9.35075 -7.51018 2.49022 8.326558181818182 -15.567899090909092 4.092707272727273 87.4365255625 56.4028036324 6.2011956484 12.249103021988997 18.352229744497517 1.0241918181818175 8.057719090909092 1.6024872727272728 1.0755028099173556 3.044405785123968 1.612303834710744 1.048968880430577 64.92683694800085 2.5679654592528927 2.035575883434261 18.39229401416228 3.28217159657215 1.4267360945298402 4.288623790234145 1.8116764602356983
user_001 Jogging 1.446047149264e12 11.11349 -2.6435 -2.0699 8.939681818181818 -10.05674090909091 2.490221 123.50965998010001 6.98809225 4.28448601 11.609575282502801 14.592364505757205 2.173808181818183 7.413240909090911 4.5601210000000005 1.1815951239669422 5.758498429752065 2.2574150991735538 4.725442011339674 54.95614077621904 20.794703534641005 2.1236015528734042 40.66845828931668 6.497162255694437 1.4572582313623774 6.377182629446697 2.5489531685957743
user_001 Jogging 1.44604714928e12 13.22111 -0.842448 -3.10454 9.89768909090909 -6.970213454545455 1.1037228181818184 174.7977496321 0.7097186327039999 9.638168611600001 13.60682317355539 13.2681391170847 3.3234209090909097 6.127765454545455 4.208262818181819 1.6344719834710744 6.779212396694215 2.755262090909091 11.045126538982649 37.549509465920664 17.709475946891583 3.6817334965635626 48.05599824849062 9.364926510827347 1.918784379903996 6.932243377759512 3.060216742459159
user_001 Jogging 1.446047149394e12 2.68302 -4.29128 2.49022 5.776515454545454 -0.11436245454545459 -1.007377090909091 7.1985963204 18.415084038400003 6.2011956484 5.640467711741643 6.993932828298204 3.0934954545454536 4.176917545454546 3.4975970909090908 4.619971983471075 2.365723818181818 2.9259617520661156 9.569714127293382 17.44664018152603 12.233185410335734 27.913373627294142 7.4033716166812065 13.02444588125051 5.283310858476353 2.7209137466449036 3.608939717043014
user_001 Jogging 1.446047149402e12 5.86361 -6.36057 5.55585 5.1494563636363635 -0.9643760909090912 -2.8467090909090942e-2 34.3819222321 40.4568507249 30.867469222500006 10.281354102427365 6.646319206470613 0.7141536363636369 5.396193909090909 5.584317090909091 4.641190991735538 2.4572493553719004 3.33323420661157 0.5100154163314058 29.11890870450982 31.184597371819375 27.93872818168933 8.299004545766735 15.748543563853593 5.285709808690724 2.880799289392917 3.9684434686478265
user_001 Jogging 1.446047149434e12 6.89825 -19.58108 9.04299 4.630389999999999 -7.005050545454545 2.9082616363636364 47.5858530625 383.4186939664 81.7756681401 22.644650917357946 10.195556118901754 2.2678600000000007 12.576029454545456 6.134728363636363 4.597170826446281 5.882325909090909 3.9169054380165282 5.143188979600003 158.15651684159488 37.634892095604485 26.575008670971535 50.825509825809554 21.491222617181755 5.155095408522672 7.129201205311122 4.635862661596195
user_001 Jogging 1.446047149563e12 3.98591 -15.74905 2.95007 11.92169909090909 -19.128203636363637 0.5080178181818181 15.8874785281 248.0325759025 8.702913004900001 16.511298175355567 23.144901652304863 7.93578909090909 3.379153636363636 2.442052181818182 5.774967107438017 0.8791501652892587 3.5599889008264465 62.97674849539172 11.418679298149584 5.963618858722944 41.11842897361307 2.1366249748332855 16.233832526013103 6.412365318165604 1.4617198687960993 4.029123046769992
user_001 Jogging 1.44604714962e12 -2.33694 -4.21464 -0.843646 2.188339454545454 -12.40473181818182 3.2217923636363635 5.461288563599999 17.7631903296 0.711738573316 4.892465377140241 13.428669191244422 4.525279454545454 8.19009181818182 4.065438363636363 6.705104636363637 4.739049917355373 2.9405300743801646 20.478154141731203 67.07760399024879 16.52778908852631 47.59374277461837 31.377978538409174 10.302701637973604 6.8988218396055405 5.601604996642406 3.209782179209923
user_001 Jogging 1.446047149669e12 -10.34589 -0.152682 0.152682 -5.040265090909091 -4.266891727272728 0.9608932727272728 107.03743989210001 2.3311793124000002e-2 2.3311793124000002e-2 10.34814299661287 8.821005416974698 5.30562490909091 4.114209727272727 0.8082112727272728 6.073611198347108 6.515719066115702 2.7058581157024792 28.149655675965928 16.926721679985526 0.653205461363438 38.210883633728024 44.103016009657594 9.257819553824516 6.181495258732148 6.641010164851248 3.042666520311504
user_001 Jogging 1.446047149782e12 8.00954 2.52974 -14.9072 -2.4971918181818182 1.1780775454545456 -3.1916349999999998 64.1527310116 6.3995844675999995 222.22461184 17.110725505343133 7.3390330778954285 10.506731818181818 1.3516624545454543 11.715565 3.812711074380165 0.9545242231404959 3.9469921652892554 110.3914134991942 1.8269913910278424 137.254463269225 20.90936161935897 1.1652671375526948 36.498496284713816 4.572675542760384 1.079475399234598 6.041398537152951
user_001 Jogging 1.446047149863e12 15.32872 -11.18893 -5.05888 8.033928999999999 -5.155224363636363 -3.5748378181818183 234.96965683840003 125.19215454489998 25.592266854400002 19.640623163171274 15.724526838982381 7.294791000000002 6.033705636363636 1.484042181818182 7.81702794214876 4.484109628099174 8.200230900826448 53.213975733681025 36.40560370628631 2.20238119741567 78.26415031479696 30.749846915804653 98.87448495723784 8.846702793402578 5.54525445005048 9.943565002414267
user_001 Jogging 1.446047149871e12 11.15181 -15.21256 16.7837 8.31958990909091 -6.768160727272726 -0.6938469090909084 124.36286627609998 231.4219817536 281.69258569 25.248315463010595 16.46430774422488 2.8322200909090895 8.444399272727274 17.477546909090908 7.11934505785124 5.128903884297521 8.724047438016528 8.021470643349092 71.30787907723692 305.46464595947316 68.9577918733565 37.06629125091457 114.16631974726042 8.304082843599074 6.088209199010376 10.684864049077106
user_001 Jogging 1.446047149944e12 9.38908 -19.58108 8.58315 6.636980909090909 -18.389666363636362 5.719578181818182 88.1548232464 383.4186939664 73.67046392249999 23.350459976953342 22.55196135090203 2.752099090909091 1.191413636363638 2.8635718181818177 2.407844991735537 5.750263537190083 6.912224115702478 7.574049406182646 1.4194664529132273 8.200043557885122 9.657153846656074 41.139247568377066 86.3043109583581 3.1075961524393856 6.413988429080385 9.290011354048934
user_001 Jogging 1.446047150016e12 6.51505 -13.14327 3.10335 7.936387272727271 -18.02736636363636 4.813826363636364 42.44587650249999 172.74554629289997 9.6307812225 14.994072296007511 20.44310503121508 1.4213372727272713 4.8840963636363615 1.7104763636363645 2.761913388429752 1.6195868595041334 1.1109719834710745 2.0201996428437976 23.85439728928593 2.9257293905586805 10.517149891811421 5.401965107519985 1.8010637061118713 3.2430155552836037 2.324212793080699 1.3420371478136779
user_001 Jogging 1.446047150073e12 17.89618 0.307161 -19.54396 8.793368181818183 -10.115964454545455 -2.2684658181818182 320.27325859240005 9.434787992100001e-2 381.96637248159993 26.501584461196295 16.630296665418793 9.102811818181818 10.423125454545456 17.275494181818182 2.970298512396694 5.750580082644627 4.936670809917355 82.86118299723059 108.64154424119342 298.4426992260339 17.678004749792333 38.80134902204815 65.50860297388387 4.204521940695795 6.229072886236615 8.09373850416999
user_001 Jogging 1.446047150219e12 7.51138 -8.12331 -0.383802 6.535954545454545 -0.10739454545454558 -3.163767454545454 56.4208295044 65.9881653561 0.147303975204 11.070514840589123 10.938402494551363 0.975425454545455 8.015915454545455 2.7799654545454544 3.8171466942148764 3.090644190082645 7.9747422314049565 0.9514548173752075 64.25490057442067 7.728207928466115 19.912643683929602 16.446861510478875 100.57568271687927 4.4623585337722025 4.0554730316547385 10.028742828334929
user_001 Jogging 1.446047150243e12 8.1245 -15.71073 7.20362 7.567119090909089 -4.44804 -3.1707347272727264 66.00750024999999 246.8270371329 51.8921411044 19.09781868400944 13.582559288601772 0.5573809090909103 11.26269 10.374354727272726 2.450916115702479 5.704342760330579 7.738803190082644 0.3106734778190096 126.84818603609999 107.62723600728596 11.5941647847275 46.82268835630012 97.03941247664534 3.405020526329834 6.842710600069253 9.850858463943402
user_001 Jogging 1.446047150268e12 12.41638 -19.4278 10.46085 8.852590909090909 -10.168218181818181 -5.2854727272726845e-2 154.1664923044 377.43941284000005 109.42938272250001 25.318674686225187 17.246433333074293 3.5637890909090917 9.25958181818182 10.513704727272728 2.286550661157025 8.188196363636363 8.710745471074379 12.70059268448265 85.73985544760333 110.53798709227691 10.773763729945234 76.44156760443937 104.44351299752908 3.282341196454938 8.7430868464427 10.2197609070628
user_001 Jogging 1.446047150284e12 10.80693 -19.58108 9.11964 8.702792727272728 -14.045537272727275 4.956656181818182 116.7897360249 383.4186939664 83.1678337296 24.15318330408851 17.820332107144218 2.1041372727272716 5.535542727272725 4.162983818181819 1.7776185950413228 8.856742892561984 6.789029223140495 4.427393662480161 30.64223328546196 17.330434270443675 4.639401814140121 82.55556567736312 59.70904086977294 2.1539270679714577 9.086009337292314 7.727162536777193
user_001 Jogging 1.446047150324e12 12.14814 -19.58108 4.29128 11.284189090909091 -18.926151818181815 5.541912636363636 147.57730545959998 383.4186939664 18.415084038400003 23.439519693551745 23.198834022657703 0.8639509090909083 0.6549281818181854 1.250632636363636 2.00912479338843 6.009955454545454 6.8473013553719 0.746411173319007 0.42893092333967403 1.5640819911378585 5.249374220344404 52.14090314037559 61.60130984272627 2.2911512870922346 7.220865816533055 7.848650192404186
user_001 Jogging 1.446047150405e12 1.07357 -10.42253 1.07237 6.981863636363636 -17.13903090909091 4.709316363636364 1.1525525448999998 108.6291316009 1.1499774169 10.532410054811766 19.27835656772833 5.908293636363636 6.716500909090909 3.6369463636363637 3.372187024793389 1.9901223966942168 1.179696619834711 34.90793369349504 45.11138446181901 13.227378851967769 14.967308917364766 8.682548946544332 2.3495476234109898 3.868760643586621 2.9466165251936554 1.5328234155997846
user_001 Jogging 1.446047150648e12 4.98224 -9.69444 13.948 6.37222090909091 -2.2289466363636365 -8.420690909090825e-2 24.8227154176 93.9821669136 194.546704 17.701739641379884 12.222252634351964 1.3899809090909097 7.465493363636364 14.032206909090908 6.154051157024793 3.024770818181818 7.538651479338842 1.9320469276371917 55.73359116249859 196.90283073953862 50.009996299928396 14.55400636162292 78.89689127763887 7.0717746216864406 3.8149713447970903 8.88239220467318
user_001 Jogging 1.446047150697e12 1.53341 -17.51178 5.55585 7.535765454545455 -9.994035454545456 3.1486354545454547 2.3513462280999997 306.6624387684001 30.867469222500006 18.435868686313647 16.111863223352596 6.002355454545455 7.517744545454546 2.4072145454545457 3.9055057851239665 6.042574842975207 8.693645363636366 36.02827100271158 56.51648305071158 5.7946818678479355 19.9161504314891 41.937659552253066 111.7926043798458 4.462751441822533 6.475929242375419 10.57320218192416
user_001 Jogging 1.446047150793e12 10.46204 -19.54276 6.24561 7.661177272727273 -19.535792727272725 5.656872090909091 109.4542809616 381.91946841760006 39.0076442721 23.030010717568068 22.099586635479316 2.800862727272727 6.967272727276708e-3 0.5887379090909093 2.9281772727272735 2.3704752066115735 2.3156854628099173 7.844832017025618 4.8542889256253815e-5 0.34661232560073574 12.87407932845424 9.62765954508025 7.460318108028584 3.588046728856 3.102847006392718 2.731358289940846
user_001 Jogging 1.446047150834e12 8.50771 -11.4955 2.8351 9.511003636363638 -17.368954545454542 3.9045884545454546 72.3811294441 132.14652025 8.03779201 14.579624196257598 20.393900266321058 1.0032936363636384 5.873454545454543 1.0694884545454544 2.6957220082644624 2.1975576033057864 1.385232033057851 1.0065981207677728 34.497468297520626 1.1438055544060244 12.06560516315159 8.839956388241845 2.4539926417881213 3.473557997666311 2.9732064153438533 1.5665224676933687
user_001 Jogging 1.446047151037e12 5.40376 -4.55952 -0.728685 4.024230909090909 -0.13526436363636377 -2.6272832727272726 29.2006221376 20.7892226304 0.530981829225 7.107800404993446 7.222132790105045 1.3795290909090907 4.424255636363636 1.8985982727272726 2.206425950413223 2.662786214876033 5.014576909090909 1.9031005126644622 19.5740379358954 3.6046754012029827 7.852155238665514 8.991493953473054 37.35349103853367 2.8021697376614276 2.9985819904536632 6.111750243468205
user_001 Jogging 1.446047151109e12 5.74865 -19.58108 6.16897 5.313188181818181 -13.79123 2.1592725454545456 33.04697682249999 383.4186939664 38.056190860899996 21.319518325933164 15.60131921150684 0.4354618181818184 5.7898499999999995 4.009697454545455 1.1201566115702482 7.893667628099174 4.2361372479338835 0.18962699509421507 33.522363022499995 16.077673676988297 1.8859984245438768 68.41907075554805 20.595874900877785 1.3733165784129588 8.271582119253127 4.538267830447844
user_001 Jogging 1.446047151206e12 1.68669 -14.5228 4.7128 7.905035454545454 -18.184130909090907 0.5498201818181818 2.8449231561 210.91171984000002 22.21048384 15.361221528123993 20.60780788439442 6.218345454545455 3.661330909090907 4.162979818181817 4.84134479338843 1.2287837190082649 3.097295462809917 38.66782019206612 13.405344025864448 17.330400966589117 28.15586054618888 3.1095243914700963 17.112671343026122 5.306209621395378 1.763384357271578 4.136746468304061
user_001 Jogging 1.446047151312e12 -3.33327 0.613724 -1.11189 -0.23629118181818212 -3.733888636363637 0.23977381818181834 11.1106888929 0.37665714817600005 1.2362993721000002 3.567021924964297 5.887372545391934 3.096978818181818 4.347612636363637 1.3516638181818184 2.5582775619834717 6.220242198347108 2.9696667768595044 9.59127780026685 18.901735635868775 1.8269950773818517 10.102695203948885 39.87971431446006 10.319315675667944 3.178473722393955 6.315038742118694 3.212369168646086
user_001 Jogging 1.446047151352e12 -0.267643 4.21583 -1.91661 -1.9084504545454544 0.4639268181818181 -1.063117090909091 7.163277544900001e-2 17.773222588900005 3.6733938920999996 4.638776698273911 3.685062453253754 1.6408074545454543 3.7519031818181823 0.8534929090909089 2.8578729834710743 4.62060579338843 2.3017529421487604 2.692249102891933 14.0767774857374 0.7284501458684625 10.69858823466386 22.2415387209836 7.617826076039722 3.2708696450124486 4.7160935869619465 2.760040955500429
user_001 Jogging 1.446047151417e12 6.9749 -0.344284 8.19995 2.5680608181818183 3.209053363636363 -3.9232039090909097 48.64923001 0.11853147265599999 67.23918000249999 10.770651859806629 9.713949174801307 4.406839181818182 3.553337363636363 12.12315390909091 3.6635484710743795 3.071323785123967 6.242726438016529 19.42023157440794 12.62620641981422 146.9708607035062 19.565297848868102 10.906363885450785 60.51857971860243 4.423267779466681 3.302478445872249 7.779368850916019
user_001 Jogging 1.446047151578e12 9.54236 -19.58108 2.60518 4.891664545454546 -19.56714545454545 4.1031581818181815 91.0566343696 383.4186939664 6.7869628323999995 21.93769110841886 21.159088532938316 4.650695454545454 1.3934545454549863e-2 1.4979781818181817 4.304542066115702 4.257987305785124 1.9438857024793388 21.628968210929752 1.9417155702491625e-4 2.2439386332033053 26.518021072216154 29.578972161291038 6.378130290521789 5.1495651342823265 5.43865536334957 2.5254960484074784
user_001 Jogging 1.446047151635e12 3.29615 -12.56846 4.63616 7.093340909090909 -17.65113 3.9045881818181813 10.864604822499999 157.9661867716 21.493979545600002 13.795824409570455 19.577262417347413 3.797190909090909 5.0826699999999985 0.7315718181818189 2.8806734710743807 1.836841239669422 1.3944159504132232 14.418658800082643 25.833534328899983 0.5351973251578523 10.559005899553947 6.313381829459578 2.739620745666492 3.2494624016218356 2.5126443897733672 1.6551799737993727
user_001 Jogging 1.446047151724e12 6.82161 2.91294 -5.4804 7.260554545454545 -3.1416661818181826 -2.338138363636364 46.53436299209999 8.485219443599998 30.034784160000005 9.222492428606271 9.907660701299607 0.43894454545454487 6.054606181818182 3.1422616363636364 1.4650392561983472 7.173182066115704 4.107240454545455 0.19267231398429702 36.65825601691095 9.873808191362677 2.9706421240818184 51.96965262163333 20.13785207873389 1.7235550829845323 7.2089980317401485 4.487521819304491
user_001 Jogging 1.446047151733e12 7.62634 3.94759 -5.02056 7.518345454545454 -1.7969716363636365 -3.101061090909091 58.1610617956 15.583466808099999 25.206022713599996 9.94738917089806 9.691493439543757 0.10799454545454612 5.7445616363636365 1.9194989090909087 1.2918062809917357 7.148796347107439 4.224101760330578 1.1662821847934029e-2 32.999988393980864 3.6844760620011887 2.6031193905631858 51.68294079364623 20.43626016398501 1.6134185416571813 7.189084837004375 4.520648201749945
user_001 Jogging 1.446047151741e12 7.24314 4.40743 -4.21583 7.685561818181816 -0.5985916363636367 -3.7246365454545454 52.4630770596 19.425439204899998 17.773222588900005 9.468988269788913 9.585143130985788 0.44242181818181603 5.006021636363636 0.49119345454545504 1.2357502479338842 6.958460958677687 4.146827280991736 0.1957370652033039 25.060252623740855 0.24127100978829802 2.5189534991681444 49.37879148738046 20.294661930347736 1.5871211356314754 7.02700444623315 4.504959703520969
user_001 Jogging 1.446047151781e12 4.48408 2.52974 -1.91661 6.466280000000001 2.944294727272727 -4.0068127272727265 20.106973446399998 6.3995844675999995 3.6733938920999996 5.493628291584715 8.398280777683723 1.9822000000000015 0.41455472727272724 2.0902027272727266 1.1502414876033058 4.31594256198347 2.787565033057851 3.929116840000006 0.17185562190416526 4.368947441098344 1.8744906532619081 24.103478716583982 11.197507370263382 1.3691203939982444 4.909529378319676 3.346267677616867
user_001 Jogging 1.44604715191e12 9.77228 -19.50444 1.53221 4.724448181818182 -13.442863636363633 1.4625413636363638 95.4974563984 380.42317971359995 2.3476674841 21.86934620870272 15.13713907118882 5.047831818181819 6.061576363636366 6.96686363636363e-2 1.8713614049586773 7.98075973553719 4.206682272727272 25.480606064648764 36.74270801219507 4.8537188927685855e-3 5.178756076771825 67.91527688974624 23.678325446133485 2.2756880446958947 8.241072557000468 4.866037961846731
user_001 Jogging 1.446047151951e12 10.92189 -19.58108 1.8771 9.577193636363639 -18.46630818181818 0.8528991818181818 119.2876811721 383.4186939664 3.52350441 22.49955287441286 21.701183277391326 1.344696363636361 1.1147718181818185 1.0242008181818183 3.7595082644628093 5.658104900826447 5.013626694214876 1.8082083103768523 1.2427162066123973 1.048987315964306 24.23546088704974 43.77931753685487 35.968189974079955 4.922952456306047 6.616594103982416 5.997348578670407
user_001 Jogging 1.446047151976e12 4.25415 -19.58108 4.67448 10.047488181818183 -19.225746363636357 1.0549519090909092 18.0977922225 383.4186939664 21.850763270399998 20.57589000406301 22.60094362279498 5.793338181818183 0.35533363636364257 3.619528090909091 4.706749008264463 3.0827260330578525 4.312143818181818 33.56276728891241 0.12626199313140937 13.100983600880006 29.278619037157853 16.794070458794213 28.49283913515609 5.410972097244437 4.098056912586038 5.337868407440941
user_001 Jogging 1.446047152016e12 4.48408 -14.98264 6.05401 7.438222727272728 -18.466307272727274 3.2914646363636364 20.106973446399998 224.4795013696 36.6510370801 16.77013750379227 20.591913786123246 2.9541427272727283 3.483667272727274 2.7625453636363635 4.226635619834712 1.3982168595041338 2.6770381157024796 8.726959253098354 12.135937667071085 7.631656886148768 20.821342925891145 3.0201815245361403 8.235197835964952 4.563040973505623 1.7378669467298526 2.8697034404211443
user_001 Jogging 1.446047152064e12 1.87829 -2.8351 -1.53341 3.9789436363636366 -11.882180000000004 2.9535480000000005 3.5279733241 8.03779201 2.3513462280999997 3.7305645098563835 13.13876244651408 2.100653636363637 9.047080000000003 4.4869580000000004 2.783764876033059 5.077600413223141 2.743226958677686 4.41274569996777 81.84965652640005 20.132792093764003 10.053849827812401 36.27602943311487 8.753824143514345 3.1707806338207 6.022958528257925 2.958686219171331
user_001 Jogging 1.446047152089e12 -1.95374 -0.229323 -1.72501 2.4844516363636364 -7.1304620909090906 1.6715598181818179 3.8170999876000002 5.2589038329e-2 2.9756595001 2.616361696331186 8.520474343812703 4.438191636363636 6.901139090909091 3.396569818181818 2.494937132231405 6.901456363636364 3.1270625537190084 19.69754500108813 47.62572075207355 11.536686529783667 7.469635887459421 51.97030174191706 11.108772798224885 2.7330634620256116 7.209043053132438 3.3329825679449456
user_001 Jogging 1.446047152121e12 -7.1653 2.41478 -1.26517 -0.9608938181818183 -1.619304454545455 -0.8958999090909091 51.34152409 5.8311624484 1.6006551288999997 7.66637734965479 5.000751755683819 6.204406181818182 4.034084454545455 0.3692700909090908 3.5713902892561977 7.143729504132231 2.8534371157024796 38.49465606898367 16.273837386405297 0.13636040004000818 16.102131490102952 53.84664377135873 10.161223564866225 4.012746128289572 7.338027239753115 3.1876674175431514
user_001 Jogging 1.446047153934e12 7.47306 -7.51018 2.52854 4.94740290909091 -6.4894690909090915 -0.23400418181818186 55.8466257636 56.4028036324 6.3935145316 10.89233418178124 11.335213146620982 2.52565709090909 1.0207109090909086 2.762544181818182 3.6483468760330577 7.761921628099173 2.983284371900827 6.378943740859366 1.041850759937189 7.631650356497488 17.440279192640055 74.55776113851049 11.886500614431673 4.1761560306866 8.634683615426248 3.4476804687255567
user_001 Jogging 1.446047154257e12 4.17751 0.307161 0.650847 5.1006838181818175 -7.677398090909091 0.23280609090909074 17.4515898001 9.434787992100001e-2 0.42360181740899994 4.239049362466778 12.33218067780529 0.9231738181818177 7.984559090909091 0.4180409090909092 3.599259553719008 8.311389859504134 3.1143959008264472 0.8522498985763958 63.753183876219005 0.1747582016735538 17.226113662722206 86.6462533486209 15.967773060603179 4.150435358215112 9.308396926894604 3.995969602061955
user_001 Jogging 1.44604715471e12 9.00587 -2.22198 -1.49509 5.100684 -7.464894272727275 -0.5545014545454546 81.1056944569 4.937195120399999 2.2352941081 9.395647060495621 11.52705378980653 3.9051859999999996 5.242914272727274 0.9405885454545454 3.9270390578512395 7.693831363636364 1.828923950413223 15.250477694595997 27.488150071167365 0.8847068118402974 21.882637782047397 70.36756291734724 7.930507014658335 4.67788817545347 8.388537591102947 2.81611558971899
user_001 Jogging 1.446047154905e12 6.74497 -15.40417 3.48655 4.529362181818183 -9.478453363636364 0.7449061818181818 45.4946203009 237.28845338890002 12.1560309025 17.173791211968894 12.247786756547718 2.2156078181818177 5.925716636363637 2.741643818181818 3.307896446280992 8.032380694214877 1.6208546280991738 4.908918003988394 35.11411765447677 7.5166108257745785 17.328900067769936 72.46294735240768 4.432801456994455 4.162799546911902 8.512517098508976 2.1054219189973433
user_001 Jogging 1.44604715497e12 5.86361 -4.7128 0.650847 4.682644000000001 -9.934813454545456 0.7449061818181818 34.3819222321 22.21048384 0.42360181740899994 7.550894509229288 12.548863588071583 1.1809659999999997 5.222013454545456 9.405918181818185e-2 3.3313320991735536 7.781240181818182 1.591401743801653 1.3946806931559994 27.269424519453768 8.847129684305791e-3 17.378211958186263 69.14624195633812 4.41771863226816 4.16871826322987 8.315421934955442 2.1018369661484595
user_001 Jogging 1.446047155164e12 5.59536 -16.13225 3.7722e-2 4.355179454545455 -6.994599818181817 8.649363636363634e-2 31.308053529600002 260.24949006249994 1.422949284e-3 17.075097848662068 10.670622017712152 1.2401805454545451 9.13765018181818 4.877163636363634e-2 3.3595184628099175 7.907918900826448 1.9141160082644626 1.5380477853239332 83.49665084528183 2.3786725135867747e-3 16.90470911643385 71.7752335717931 6.28171303261849 4.111533669621817 8.472026532760216 2.506334581140054
user_001 Jumping 1.446047227671e12 0.575403 -0.727487 2.95007 2.4530965 -6.7246185 -0.11555499999999985 0.331088612409 0.529237335169 8.702913004900001 3.092448698439151 8.451269086203546 1.8776935 5.9971315 3.065625 0.93884675 2.99856575 1.5328125 3.52573287994225 35.96558622829225 9.398056640624999 1.762866439971125 17.982793114146126 4.699028320312499 1.3277298068399026 4.240612351317452 2.1677242260750096
user_001 Jumping 1.446047228771e12 5.63368 -19.58108 5.59417 2.6865030909090906 -9.102216272727274 1.904966727272727 31.7383503424 383.4186939664 31.2947379889 21.129405630488048 10.699369611977787 2.9471769090909095 10.478863727272726 3.6892032727272728 2.3082646342516067 7.828420587603304 1.485286929476584 8.685851733478646 109.80658501475205 13.61022078750162 7.602895554261843 72.41129287122205 4.47538958087852 2.757334864368462 8.509482526641795 2.1155116593577357
user_001 Jumping 1.446047229678e12 1.64837 -0.727487 -0.307161 1.7702999999999998 -6.9388619090909085 0.6334268181818182 2.7171236568999997 0.529237335169 9.434787992100001e-2 1.8277606167083258 8.146417896575903 0.12192999999999987 6.2113749090909085 0.9405878181818182 1.3057419917355373 6.445413867768594 0.7844572066115703 1.4866924899999969e-2 38.581178261284094 0.8847054437120332 2.9860285836722325 51.402171643057116 0.9434764262345948 1.7280129003199693 7.169530782628464 0.9713271468638127
user_001 Jumping 1.446047229743e12 2.56806 -1.03405 0.267643 1.8504245454545454 -6.945829272727272 0.5149825454545455 6.5949321636 1.0692594024999997 7.163277544900001e-2 2.7813349926876842 8.172391978699084 0.7176354545454546 5.911779272727272 0.24733954545454545 1.3551465785123966 6.545490115702479 0.7524711818181818 0.5150006456206613 34.949134169447795 6.1176850745661156e-2 3.0300886047082014 52.47526121010706 0.9163994449650903 1.7407149694042967 7.243981033251472 0.9572875456022032
user_001 Jumping 1.44604723039e12 2.68302 0.268841 0.535886 2.156987 -6.151554090909091 0.6194927272727272 7.1985963204 7.2275483281e-2 0.287173804996 2.749189991375096 7.848112788192466 0.526033 6.420395090909091 8.360672727272722e-2 2.190274570247934 6.47613232231405 0.7508874214876033 0.27671071708899997 41.221473123369556 6.990084845256189e-3 7.960874463777737 50.38179048260929 1.1474568828624658 2.8215021644113154 7.0980131362663235 1.071194138736049
user_001 Jumping 1.446047231296e12 11.61165 -19.58108 4.25296 3.6932826363636355 -7.416123636363636 0.5672378181818182 134.8304157225 383.4186939664 18.0876687616 23.158945970196918 9.36085068307741 7.918367363636364 12.164956363636364 3.6857221818181816 3.0409216446280993 7.26154073553719 1.760200305785124 62.70054170550149 147.98616332917686 13.584548001546576 12.618763656560139 62.25575293517845 3.835529500171806 3.5522899173012523 7.890231488060312 1.9584507908476552
user_001 Jumping 1.446047231555e12 -0.919089 -0.382604 0.114362 2.867653454545455 -6.116717181818182 0.5776896363636365 0.8447245899210001 0.146385820816 1.3078667044000002e-2 1.0020923499263927 8.397198505817867 3.786742454545455 5.7341131818181825 0.46332763636363644 3.6160431239669424 7.249506231404959 1.7985207438016528 14.339418417056937 32.88005398190104 0.2146724986183141 15.941718766454512 64.06415040944202 4.433685351168226 3.99270819951252 8.004008396387526 2.1056318175712074
user_001 Jumping 1.446047232332e12 1.45677 -0.957409 0.420925 2.8606881818181815 -6.423278727272727 -0.7321674545454546 2.1221788328999995 0.9166319932809999 0.177177855625 1.7933177860619125 8.07830938399036 1.4039181818181816 5.465869727272727 1.1530924545454546 1.8216388347107435 6.94674379338843 1.2500011074380166 1.970986261239669 29.875731875516436 1.3296222087296614 5.385024996212779 55.70059721558134 2.283652852964922 2.3205656629823643 7.463283273170149 1.5111759834529273
user_001 Jumping 1.446047232397e12 1.18853 -2.95007 0.650847 2.84327 -6.649717363636362 -0.7008144545454547 1.4126035609000003 8.702913004900001 0.42360181740899994 3.246400835264956 8.238312707705102 1.6547399999999999 3.6996473636363616 1.3516614545454546 1.8213221900826446 6.764009280991735 1.3415265041322315 2.7381644675999994 13.687390615261481 1.826988687703934 5.383975964246953 53.981179779284275 2.438929673971659 2.320339622608499 7.347188562932374 1.5617072945887327
user_001 Jumping 1.446047233692e12 1.91661 -2.95007 -0.345482 2.4530986363636362 -6.485985363636365 0.1108783636363636 3.6733938920999996 8.702913004900001 0.119357812324 3.534920750076867 7.642951055439692 0.5364886363636363 3.5359153636363647 0.4563603636363636 1.7491166859504133 6.342802719008264 0.7968080991735537 0.287820056947314 12.502697458799686 0.20826478149831404 5.789309919787663 48.261592607761735 0.9506238958732367 2.406098485055768 6.947056398775077 0.9749994337809826
user_001 Jumping 1.446047233755e12 4.75232 -14.63776 -3.67935 2.6690859090909087 -7.51714990909091 -9.117436363636361e-2 22.5845453824 214.2640178176 13.5376164225 15.823595660357984 8.689111065763383 2.0832340909090914 7.12061009090909 3.588175636363636 1.9223500247933885 6.688635768595042 1.0153291322314049 4.3398642775258285 50.70308806675636 12.875004397393585 6.1809734275535115 51.871071733025275 1.99354172880025 2.486156356216059 7.202157436006607 1.411928372404298
user_001 Jumping 1.446047234273e12 1.11189 -0.152682 1.83878 2.8850731818181816 -5.9042141818181815 -1.0387308181818182 1.2362993721000002 2.3311793124000002e-2 3.3811118884000004 2.1542337509249085 7.883338389741368 1.7731831818181816 5.751532181818181 2.8775108181818183 1.935018611570248 6.829566181818181 1.9438862644628097 3.1441785962828503 33.08012243849021 8.280068508753397 7.562639685647951 53.59971111397245 5.921171536456791 2.75002539727326 7.321182357650467 2.433345749468577
user_001 Jumping 1.446047235892e12 2.79798 -9.88604 -0.843646 2.5436736363636365 -6.792547727272727 -0.22703800000000005 7.8286920804 97.7337868816 0.711738573316 10.308938720126141 8.932443619128527 0.2543063636363634 3.0934922727272722 0.6166079999999999 2.782497404958678 6.824814438016529 1.274070082644628 6.467172658595029e-2 9.569694441423344 0.3802054256639999 12.44456348689605 56.63568852073141 2.3675355543652423 3.5276852873939943 7.525668642767326 1.5386798089158258
user_001 Jumping 1.446047236475e12 7.58802 -18.54643 3.10335 3.8918525454545456 -8.22433409090909 0.22583972727272725 57.578047520400006 343.9700657449 9.6307812225 20.277546559872572 9.852919901323235 3.6961674545454546 10.32209590909091 2.8775102727272728 3.0387060082644624 7.119976330578512 1.2436688099173556 13.661653852041026 106.5456639564713 8.280065369650984 13.376589391758433 61.73144706318171 2.1075806428934363 3.6574020002945304 7.856936238966288 1.451750888717977
user_001 Jumping 1.44604723654e12 0.767005 -6.28393 0.689167 3.000034818181818 -7.029436818181818 0.4313758181818182 0.5882966700250001 39.4877762449 0.47495115388899994 6.36796859829051 8.41581035264782 2.233029818181818 0.7455068181818181 0.2577911818181818 2.5535269834710745 6.1192146776859495 1.1641781074380164 4.986422168889123 0.5557804159555784 6.645629342321485e-2 8.620365385624313 49.222535047066316 1.9970901490347777 2.9360458759400054 7.015877354049621 1.4131844002234024
user_001 Jumping 1.446047236669e12 0.11556 -0.114362 -1.11189 2.7248254545454547 -5.9285986363636365 0.25719245454545453 1.3354113599999998e-2 1.3078667044000002e-2 1.2362993721000002 1.1237135545787458 7.6763875816918326 2.6092654545454548 5.814236636363637 1.3690824545454545 2.8306369256198347 6.579058876033057 1.2154832396694213 6.808266212284298 33.805347663633135 1.8743867673442065 9.500612024374687 54.4232190659533 2.0968215239484818 3.082306283349318 7.37720943622677 1.4480405809052734
user_001 Jumping 1.446047238481e12 -0.152682 -1.41725 -1.22685 2.965196909090909 -5.3712112727272725 -9.465809090909091e-2 2.3311793124000002e-2 2.0085975625 1.5051609225 1.8807100462655055 7.1786868769543135 3.117878909090909 3.9539612727272724 1.1321919090909092 2.806566479338843 6.916658347107438 0.8180279834710745 9.721168891753916 15.633809746227072 1.2818585190109175 11.351055486512807 57.72618438699079 0.722933934470577 3.3691327499095087 7.597774962907943 0.8502552172557232
user_001 Jumping 1.4460472401e12 0.996927 3.8919e-2 -0.575403 2.8502368181818176 -5.782284454545455 -0.1294945454545454 0.993863443329 1.514688561e-3 0.331088612409 1.1517233801130375 7.488380834527844 1.8533098181818177 5.821203454545455 0.4459084545454546 2.339439314049587 6.414693702479339 1.1030548595041323 3.434757282169122 33.88640965921194 0.19883434983511572 7.295192824921782 51.8848540825245 1.7815078835544063 2.700961463057513 7.20311419335585 1.3347313900386124
user_001 Jumping 1.446047240295e12 7.39642 -19.58108 1.11069 3.553936818181818 -7.583338636363636 4.817236363636368e-2 54.7070288164 383.4186939664 1.2336322760999998 20.960900626139612 9.373704248824843 3.842483181818182 11.997741363636365 1.0625176363636362 2.5554266694214878 7.10002667768595 1.1068550661157026 14.76467700255558 143.945797828711 1.1289437275837682 8.508716858026242 63.728283208053945 1.7536987839118117 2.91697049317031 7.982999637232482 1.3242729265192321
user_001 Jumping 1.446047241459e12 -0.765807 -1.41725 7.6042e-2 2.278915636363636 -6.360573545454546 0.5358855454545455 0.586460361249 2.0085975625 5.782385764e-3 1.6127120975279499 8.104703046272826 3.044722636363636 4.943323545454546 0.4598435454545455 2.9804321652892556 7.0712062975206615 1.907149148760331 9.27033593238513 24.4364476750453 0.21145608629620663 10.990495416205771 58.02346179023377 6.027803224777757 3.315191610782968 7.617313292115125 2.4551584928019934
user_001 Jumping 1.446047242042e12 -0.995729 0.460442 0.305964 2.6307664545454545 -5.716095636363637 1.1420436363636361 0.991476241441 0.21200683536400003 9.361396929600001e-2 1.1389016841242268 7.747039606303429 3.6264954545454544 6.176537636363637 0.8360796363636361 3.649297107438016 6.35737238016529 2.1823569752066114 13.151469281838843 38.1496171734165 0.69902915834195 16.822933334173815 47.906882437427655 9.32110861503425 4.101576932616749 6.921479786680566 3.0530490685598637
user_001 Jumping 1.446047243531e12 11.57333 -19.58108 0.344284 4.5014933636363645 -7.2245218181818185 4.817318181818176e-2 133.9419672889 383.4186939664 0.11853147265599999 22.748168997261207 9.614007130330195 7.071836636363636 12.356558181818182 0.2961108181818182 3.602741462809917 7.240955603305786 1.341211165289256 50.01087341141494 152.68453010065784 8.768161664430581e-2 18.025288293019972 62.05824709530112 2.6538719664479 4.245619895023573 7.877705699967543 1.6290708905532318
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user_001 Walking 1.446047196533e12 -1.76214 -6.66714 0.344284 2.8084324545454553 -8.987257272727273 0.6961345454545455 3.1051373796000004 44.4507557796 0.11853147265599999 6.904666873344144 9.701824413344516 4.570572454545456 2.3201172727272734 0.35185054545454547 1.2557032892561986 1.4583886776859503 1.0045616694214876 20.890132562249672 5.382944159207441 0.12379880633666117 3.183751993088845 3.0256084847150273 1.6856977704544505 1.7843071465106126 1.7394276313532067 1.2983442418921303
user_001 Walking 1.446047196855e12 4.10087 -12.56846 2.75846 2.508837 -9.614316363636364 0.22583909090909088 16.817134756899996 157.9661867716 7.609101571599999 13.505273899484601 10.186220027020314 1.5920329999999994 2.9541436363636357 2.532620909090909 1.4302031735537193 2.04206132231405 1.1198392231404959 2.534569073088998 8.726964624267765 6.414168669164462 3.542454035813926 5.100414254760933 2.3829433887385965 1.882140811898495 2.2584096738105184 1.5436785250623255
user_002 Jogging 1.446034900214e12 -12.56846 -11.8787 -0.767005 -8.356712727272727 -6.796032454545454 -1.251235818181818 157.9661867716 141.10351369 0.5882966700250001 17.310632487914038 12.83587713807946 4.211747272727273 5.082667545454546 0.4842308181818179 6.308627701856225 4.529697224016136 2.365780258618654 17.73881508932562 25.83350937761694 0.23447948527703277 55.93649891061648 28.580764721688173 8.90295637602717 7.479070725071162 5.346098083807308 2.983782226642415
user_002 Jogging 1.446034900667e12 0.230521 -0.229323 1.91542 -9.39136081818182 -6.583527272727272 -1.310457 5.3139931441e-2 5.2589038329e-2 3.6688337763999996 1.9428233955174616 12.346907069341832 9.62188181818182 6.354204272727272 3.2258769999999997 5.931096528925619 4.556951537190082 2.00754094214876 92.58060972305788 40.37591193954552 10.406282419128997 44.472090029696176 27.521761508496457 6.616798714486936 6.668739763230844 5.246118708959649 2.5723138833522894
user_002 Jogging 1.446034901479e12 3.2195 -2.03038 -3.44943 3.863981818181818 -0.6543304545454546 -2.5018709090909095 10.36518025 4.1224429444 11.8985673249 5.136749022416805 4.770432523840446 0.6444818181818182 1.3760495454545456 0.9475590909090905 3.242975231404958 1.182546305785124 1.6772268595041324 0.41535681396694213 1.8935123515456616 0.8978682307644621 15.171435204052345 1.8653871867810379 3.1895666684273536 3.8950526574171427 1.3657917801704027 1.7859357962780615
user_002 Jogging 1.446034901487e12 2.4531 -3.02671 -2.41478 3.8395963636363635 -0.8668343636363637 -2.578511818181818 6.01769961 9.1609734241 5.8311624484 4.583648708452689 4.894646629782785 1.3864963636363634 2.1598756363636364 0.16373181818181815 2.7435442396694216 1.2234004545454547 1.544531 1.922372166376859 4.665062764557224 2.6808108285123956e-2 11.042772225629728 2.0235072717044216 2.9524234973187875 3.3230666899160672 1.4225003591227743 1.7182617662390056
user_002 Jogging 1.446034901511e12 -5.05768 -3.75479 0.919089 1.9619021818181819 -1.8875475454545454 -1.6274715454545452 25.580126982400003 14.098447944099998 0.8447245899210001 6.365791350368076 5.146547077019307 7.0195821818181825 1.8672424545454545 2.546560545454545 2.8376038925619835 1.3266429338842978 1.7925044545454545 49.274534007299316 3.4865943840569336 6.48497061166575 12.369201859800137 2.4836913994747216 4.655043712006266 3.5169876115505634 1.5759731595032707 2.157555031049328
user_002 Jogging 1.446034901705e12 -10.88237 -9.69444 0.650847 -13.599630000000001 -14.836330000000002 1.3406110000000002 118.4259768169 93.9821669136 0.42360181740899994 14.588754077984488 20.226008797532412 2.7172600000000013 5.141890000000002 0.6897640000000003 1.0017113223140504 1.4735917355371904 1.4628225206611574 7.3835019076000075 26.43903277210002 0.47577437569600034 1.8488825679129242 4.517931804138544 3.464271909362718 1.3597362126210084 2.125542708142686 1.8612554659053975
user_002 Jogging 1.446034901818e12 -4.9044 4.25415 -4.02423 -2.5459604545454546 -0.9399914545454545 -3.32749890909091 24.05313936 18.0977922225 16.1944270929 7.6384133611241545 6.456276805127437 2.3584395454545453 5.194141454545455 0.6967310909090902 3.4811313636363637 5.85255605785124 2.658035719008265 5.562237089563842 26.97910544982757 0.48543421303937095 19.643839108268764 36.150759753165694 9.796203638719605 4.432137081394117 6.012550187163987 3.1298887582020556
user_002 Jogging 1.446034901891e12 -2.68182 -7.6042e-2 0.995729 -2.866457272727273 0.28625900000000004 -0.9202861818181817 7.192158512400001 5.782385764e-3 0.991476241441 2.861715768486626 3.8984117439855948 0.1846372727272727 0.36230100000000004 1.9160151818181816 0.8411455785123967 2.412912652892562 2.5782283223140494 3.409092248016528e-2 0.13126201460100004 3.6711141769577598 1.2003020330379224 8.579598918568538 7.813035155574325 1.095582964926857 2.9290952389037366 2.795180701774811
user_002 Jogging 1.446034902086e12 -12.18526 -5.82409 -3.90927 -15.679373636363636 -11.188933636363636 -7.281458181818183 148.4805612676 33.9200243281 15.282391932899998 14.059977863730795 20.738969766811962 3.494113636363636 5.364843636363636 3.3721881818181836 1.291173578512396 2.5475080991735535 2.0585292561983475 12.208830103822311 28.7815472426314 11.371653133594227 3.1528208578917405 9.549293706104132 6.302081652987829 1.7756184437800089 3.0901931502907924 2.5103947205544848
user_002 Jogging 1.446034902093e12 -10.84405 -5.36425 -3.0279 -15.156823636363638 -10.258795454545453 -6.63698 117.59342040249999 28.7751780625 9.16817841 12.471438444501901 19.607507902411463 4.312773636363639 4.894545454545453 3.6090800000000005 1.361796363636363 2.6593016528925615 1.946102314049587 18.600016438513247 23.956575206611554 13.025458446400004 3.7071176739448544 10.506175181325467 5.351528005717207 1.925387668482598 3.241323060314332 2.313336984902374
user_002 Jogging 1.446034902134e12 -4.7128 -1.53221 -3.8919e-2 -11.13319181818182 -5.541913636363636 -3.292661363636364 22.21048384 2.3476674841 1.514688561e-3 4.955770980650841 12.951905235740183 6.42039181818182 4.009703636363636 3.253742363636364 3.776608512396695 4.219351074380165 2.819235206611571 41.22143109897605 16.077723251467763 10.586839368921954 18.97191993703419 18.784659065863558 9.502431067894996 4.3556767484553065 4.334127255384129 3.0826013475464187
user_002 Jogging 1.446034902207e12 1.57173 1.22685 -1.15021 -1.2779061818181818 1.5125097272727273 -0.7844235454545454 2.4703351929000004 1.5051609225 1.3229830441 2.3018425574960597 3.8283394853389723 2.849636181818182 0.28565972727272726 0.36578645454545455 5.201743809917356 3.6169951900826445 1.362746347107438 8.120426368727308 8.160147978552892e-2 0.13379973032893389 28.055339392748365 15.483652908929882 3.2315638527796566 5.296729122085475 3.9349273066893984 1.7976550983933643
user_002 Jogging 1.446034902231e12 -1.57053 -0.344284 -1.11189 -0.11784572727272725 1.8573923636363632 -1.1467244545454547 2.4665644809 0.11853147265599999 1.2362993721000002 1.9548389513348663 2.9538514725909635 1.4526842727272726 2.201676363636363 3.483445454545464e-2 3.776608099173554 2.949714570247934 0.648277909090909 2.110291596229165 4.847378810195039 1.2134392234793455e-3 17.801429361908134 11.44855536925793 0.6513529155226738 4.219174014177199 3.383571392664551 0.8070643812749227
user_002 Jogging 1.446034902256e12 -3.83143 -3.06503 0.765807 -0.5184667272727271 0.7217173636363636 -0.955123 14.6798558449 9.3944089009 0.586460361249 4.965956615502093 3.1629018655347987 3.312963272727273 3.7867473636363638 1.72093 3.1571484297520653 2.450917454545454 0.6970493388429753 10.975725646439804 14.339455596006951 2.9616000649000003 12.507239421669167 7.526855335243767 0.6687449028157296 3.5365575665707984 2.743511497195477 0.8177682451744686
user_002 Jogging 1.446034902345e12 -15.97897 -10.69077 6.39889 -5.165675545454545 -10.286663636363636 1.2361025454545453 255.3274822609 114.2925631929 40.945793232099994 20.26242430426083 12.469106604092127 10.813294454545456 0.4041063636363642 5.162787454545454 3.1954709173553724 5.691991082644628 2.7904146694214877 116.92733696070351 0.16330195313140541 26.65437430081193 25.07221924340757 44.56485109069431 11.683520211182929 5.007216716241426 6.675691057163618 3.4181164712722896
user_002 Jogging 1.446034902498e12 -6.82042 -10.001 -1.83997 -16.61648272727273 -13.056175454545455 -2.1430520000000004 46.51812897640001 100.020001 3.3854896009 12.244330099164266 21.356799196416535 9.796062727272728 3.0551754545454557 0.3030820000000003 5.281235619834711 2.6536024793388435 0.7819239338842976 95.96284495666201 9.334097058057031 9.185869872400018e-2 33.41177882625298 8.344735970507742 0.8624901692798557 5.780292278618182 2.888725665498152 0.9287034883534441
user_002 Jogging 1.446034902563e12 -6.13065 2.56806 -0.843646 -5.507074545454545 -5.120387272727272 -1.7145605454545458 37.5848694225 6.5949321636 0.711738573316 6.700114936284003 8.609060382124232 0.6235754545454553 7.688447272727272 0.8709145454545458 6.654433884297521 5.132704710743802 0.6333935123966944 0.38884634751157116 59.112221465507425 0.7584921454842982 56.966221893599545 28.942580369569132 0.4629910177542008 7.5475970940160515 5.379830886707233 0.6804344331044695
user_002 Jogging 1.446034902636e12 -1.91542 -3.21831 -0.805325 -4.4550090909090905 2.167436727272727 -3.0209355454545457 3.6688337763999996 10.357519256099998 0.6485483556249999 3.830783390916928 6.343693907563632 2.5395890909090904 5.385746727272727 2.215610545454546 0.8652156198347104 4.714664330578511 1.6072363801652896 6.44951275066446 29.006267810328886 4.90893008912939 1.2775306625186316 27.85260437570646 3.7260181570074264 1.1302790197639836 5.277556667218881 1.9302896562452554
user_002 Jogging 1.446034902724e12 -10.72909 -13.06663 -2.18486 -2.8072346363636367 -8.112857272727272 -0.1643308181818182 115.11337222809999 170.7368195569 4.7736132196000005 17.047692072670717 9.016976973428022 7.921855363636363 4.953772727272728 2.0205291818181816 3.2974469504132227 5.105151702479339 2.174439933884298 62.755792402374205 24.539864233471082 4.0825381745788505 15.810664296136576 26.721917705194425 5.090688727973939 3.9762626040210893 5.1693246856039545 2.256255466026385
user_002 Jogging 1.446034902838e12 -14.75272 -6.20729 -5.63368 -16.456233636363635 -10.419043636363636 -6.685749999999999 217.6427473984 38.530449144100004 31.7383503424 16.967956473450183 20.801511960308556 1.7035136363636347 4.211753636363635 1.0520699999999987 3.477331760330579 2.66595388429752 2.398978809917355 2.9019587092768537 17.738868693422305 1.1068512848999974 16.279098479944164 8.922266403834108 8.364879422591338 4.034736482094483 2.98701630458123 2.8922101276690353
user_002 Jogging 1.446034902895e12 -6.28393 -1.49389 1.49389 -12.14694 -4.357465454545455 -3.3414327272727267 39.4877762449 2.2317073320999996 2.2317073320999996 6.6295694361775865 13.545898291325244 5.863010000000001 2.863575454545455 4.835322727272727 3.6717825619834716 4.083489504132231 2.464850578512396 34.37488626010001 8.20006438387521 23.380345876880163 17.17902520682983 17.189657202903156 8.621513752262207 4.144758763405879 4.14604114824047 2.9362414328972006
user_002 Jogging 1.446034903033e12 -2.60518 -5.2876 1.49389 3.620127363636364 -2.727110272727273 -1.8957118181818187 6.7869628323999995 27.958713760000002 2.2317073320999996 6.080903216176032 6.2024957720086515 6.225307363636364 2.5604897272727274 3.3896018181818186 4.484111008264463 2.532939611570248 2.103498991735538 38.75445177174513 6.556107643469166 11.489400485821491 24.000768885446554 7.253680017838164 5.325640246205652 4.89905795898013 2.693265679029487 2.307734873464812
user_002 Jogging 1.446034903138e12 -12.41518 -11.72542 -7.7239e-2 -12.181777272727272 -11.728901818181818 0.26764363636363636 154.1366944324 137.4854741764 5.965863121e-3 17.07712313218831 17.103313548749245 0.23340272727272726 3.4818181818181415e-3 0.34488263636363636 5.954216669421488 3.747789917355372 1.2268833388429752 5.4476833098347104e-2 1.2123057851239389e-5 0.11894403286513223 41.42377563739363 22.08025245799609 2.545374521742775 6.4361304863554185 4.698962913026245 1.595422991479932
user_002 Jogging 1.446034903162e12 -15.82569 -12.79839 -0.345482 -13.836517272727274 -12.927282727272729 0.260676 250.4524639761 163.7987865921 0.119357812324 20.35609511621824 19.101228338167484 1.9891727272727255 0.1288927272727296 0.606158 4.112941603305784 2.917411074380166 1.1961640578512398 3.956808138925613 1.6613335143802255e-2 0.36742752096399994 25.41021847834294 18.272074295056942 2.3195890725029122 5.040854935260778 4.274584692699039 1.5230197216395172
user_002 Jogging 1.446034903332e12 -3.90807 2.68302 -4.59904 -3.5248700000000004 -3.507452363636365 -2.418262727272727 15.2730111249 7.1985963204 21.151168921599997 6.604754073158213 6.2858962299151155 0.38319999999999954 6.190472363636365 2.1807772727272727 4.531614380165291 4.087922247933886 1.003929388429752 0.14684223999999965 38.3219480849456 4.755789513243801 26.03798165480255 17.937086675501124 1.3010746092393464 5.1027425620741 4.23521979069577 1.1406465750789534
user_002 Jogging 1.446034903364e12 -3.06503 3.90927 -2.52974 -2.817686363636364 0.32109581818181826 -3.007001818181818 9.3944089009 15.282391932899998 6.3995844675999995 5.574619745005035 5.520307117261362 0.24734363636363632 3.5881741818181814 0.47726181818181823 2.2561485950413216 4.613006 1.049850388429752 6.117887444958676e-2 12.874993959066575 0.22777884309421492 10.156513458621633 22.844478304031163 1.672854066725234 3.186928530516747 4.779589763152394 1.2933885984982372
user_002 Jogging 1.446034903559e12 -19.58108 -10.42253 -8.81427 -15.08018545454545 -16.330821818181818 -7.13166 383.4186939664 108.6291316009 77.69135563290001 23.86920989895141 23.72028927423204 4.50089454545455 5.908291818181818 1.6826100000000004 5.996020479338842 5.813287438016529 3.578356950413223 20.258051709302517 34.90791220879421 2.8311764121000014 37.15895341889142 43.90340861610187 14.47403434508205 6.095814418015974 6.625964730973284 3.80447556768105
user_002 Jogging 1.446034903656e12 -3.02671 -2.10702 1.83878 -12.244483636363638 -4.859112727272729 -3.811728818181818 9.1609734241 4.439533280399999 3.3811118884000004 4.120875949710206 13.973353179277263 9.217773636363638 2.752092727272729 5.650508818181818 5.437367190082646 4.621873553719009 3.1761519338842983 84.96735081124054 7.574014379507447 31.928249904350483 40.0247632765042 23.596950291099475 14.544418632169284 6.326512726337015 4.857669224134089 3.81371454518679
user_002 Jogging 1.446034903664e12 -1.91542 -1.37893 2.98839 -10.638514545454544 -4.179798181818182 -2.759661545454545 3.6688337763999996 1.9014479449 8.9304747921 3.8079858867122915 12.21590272555592 8.723094545454545 2.8008681818181818 5.748051545454545 5.953899504132232 4.342230082644629 3.610976867768595 76.09237844893885 7.844862571921487 33.04009656920238 46.1014217529465 21.170256110268816 17.46341094419858 6.789802777175969 4.6011146595437955 4.178924615759248
user_002 Jogging 1.446034903688e12 1.68669 0.767005 1.8771 -5.3433436363636355 -2.4519013636363636 -0.11207609090909067 2.8449231561 0.5882966700250001 3.52350441 2.637560281040985 7.330031668983031 7.030033636363635 3.2189063636363637 1.9891760909090908 7.687181198347106 3.4079735454545457 4.361865388429752 49.42137292840412 10.361358177858678 3.9568215206443713 62.03217093976252 12.505821022904898 21.370784418181128 7.876050465795818 3.5363570270696507 4.622854574630391
user_002 Jogging 1.446034903802e12 -8.81307 -2.91174 -1.61005 -0.5846565454545452 -0.9922438181818181 -2.317236909090909 77.6702028249 8.4782298276 2.5922610025 9.420227898251719 6.299631258550062 8.228413454545455 1.919496181818182 0.7071869090909091 4.653226421487603 2.1057176611570245 1.7256828016528922 67.70678797894467 3.684465592014579 0.5001133243895537 30.638221487023472 5.665971506206171 4.285349532874221 5.535180348193134 2.38033012546709 2.0701085799721284
user_002 Jogging 1.446034903891e12 -14.79104 -15.78737 2.56686 -12.119069090909091 -13.000438181818181 1.3022913636363636 218.77486428160003 249.24105151689997 6.5887702596 21.785423706187125 18.60925747454722 2.671970909090909 2.7869318181818183 1.2645686363636366 4.556316016528926 5.576081132231405 3.255326991735537 7.1394285390281 7.766988959194216 1.5991338360745873 23.245656366794588 43.7177504637925 17.94761159847474 4.821374945676242 6.61193999245248 4.236462155912022
user_002 Jogging 1.446034904036e12 -4.5212 -3.86975 -2.72134 -8.691145454545454 -7.99092909090909 -1.2721353636363635 20.441249440000004 14.974965062499999 7.405691395600001 6.543844886463921 12.128840394525708 4.169945454545454 4.121179090909091 1.4492046363636366 3.656264380165289 2.8268364462809914 1.3494450743801654 17.388445093884293 16.984117099346278 2.1001940780578603 14.260647954712015 10.580754409606984 2.6915586961146896 3.776327310325737 3.2528071583798175 1.6405970547683821
user_002 Jogging 1.446034904052e12 -2.4519 -2.4519 -1.34181 -7.266326363636364 -6.377991818181818 -1.7737833636363638 6.011813610000001 6.011813610000001 1.8004540760999999 3.7180749449278188 10.010471067635713 4.814426363636364 3.926091818181818 0.43197336363636385 3.708519669421487 3.345269421487602 1.1705110743801652 23.17870121087686 15.414196964794213 0.18660098689131424 14.723721494830494 13.114459041074072 2.413511653743653 3.8371501788215814 3.62138910379347 1.5535480854301398
user_002 Jogging 1.446034904068e12 -3.06503 1.03525 -1.03525 -6.00524 -4.249472 -1.9235813636363637 9.3944089009 1.0717425625 1.0717425625 3.3967475658193975 7.858612444492149 2.9402099999999995 5.284722 0.8883313636363637 3.514700991735537 4.132577867768595 1.2607692396694217 8.644834844099996 27.928286617284005 0.7891326116200413 13.18482379751194 17.94021551195684 2.50944098331362 3.6310912681330305 4.235589157597422 1.584121517849442
user_002 Jogging 1.446034904141e12 -4.98104 -1.03405 0.459245 -4.110125454545455 2.7248261818181816 -2.1918236363636363 24.8107594816 1.0692594024999997 0.210905970025 5.107927647698722 6.009845601241079 0.8709145454545455 3.7588761818181817 2.6510686363636364 2.47118347107438 4.154430181818182 1.6769083719008264 0.7584921454842976 14.129150150240033 7.028164914710951 7.068442432986174 20.290694168959366 3.8194279934045157 2.658654252246082 4.504519304982427 1.9543356910736998
user_002 Jogging 1.44603490419e12 -0.842448 -2.18366 1.30229 -3.5527377272727274 -0.22583909090909077 -0.26884136363636374 0.7097186327039999 4.768370995600001 1.6959592440999998 2.6784415006499582 5.301884437122247 2.7102897272727273 1.9578209090909093 1.5711313636363637 2.3647737520661156 3.229358115702479 2.469601165289256 7.345670405760075 3.8330627120735548 2.46845376180186 6.257878597314694 12.208151199119698 6.884548005580183 2.5015752231973147 3.4940164852386855 2.623842221929547
user_002 Jogging 1.44603490423e12 -9.2346 -13.33487 -3.87095 -2.5668612727272726 -4.789440909090909 -0.23748899999999998 85.27783716 177.8187579169 14.9842539025 16.675756324059186 5.879288130861806 6.667738727272727 8.545429090909092 3.633461 2.5617600909090905 4.247855041322314 2.637450396694215 44.45873973517253 73.0243583477554 13.202038838521 8.912440530990839 21.106037928951018 7.913980677297903 2.985371087652394 4.594130813217123 2.813179816026324
user_002 Jogging 1.446034904247e12 -11.4955 -11.95534 -4.94392 -4.120576727272727 -6.674103636363637 -1.198980909090909 132.14652025 142.93015451559998 24.442344966400004 17.306617801638772 8.42209099464779 7.374923272727273 5.281236363636363 3.7449390909090914 3.597359198347107 4.569618677685951 2.868639413223141 54.389493278614346 27.89145752859503 14.024568794619011 18.159871432887243 25.35676770577446 9.372191101671767 4.26144006562186 5.035550387571796 3.061403452939806
user_002 Jogging 1.446034904433e12 -4.48288 -1.80046 1.45557 -9.56206 -3.9951645454545455 -3.5992239999999995 20.0962130944 3.2416562115999996 2.1186840249000003 5.045448773984332 11.195118393459259 5.079180000000001 2.1947045454545453 5.054793999999999 5.569430082644629 2.9224779338842977 3.0064033553719 25.79806947240001 4.816728041838842 25.550942382435995 33.99202304405725 8.910876950714426 11.493034828647646 5.830267836391159 2.9851092024772603 3.390137877527645
user_002 Jogging 1.446034904538e12 2.72134 -2.6435 -3.41111 3.4424599090909087 -0.6090423636363635 -1.4950904545454546 7.405691395600001 6.98809225 11.635671432099999 5.101907004023103 4.55478708374157 0.7211199090909086 2.034457636363636 1.9160195454545452 4.419503983471075 1.9660537438016528 2.015458099173554 0.5200139232872804 4.139017874158314 3.6711308985638422 22.016640849837806 4.48710159313118 4.649995244198693 4.692189345053949 2.1182779782481758 2.1563847625594774
user_002 Jogging 1.446034904684e12 -12.22358 -12.76007 -0.767005 -8.001378818181818 -5.799704 -0.22703663636363636 149.4159080164 162.81938640490003 0.5882966700250001 17.686819699746053 10.153678420593193 4.222201181818182 6.9603660000000005 0.5399683636363637 6.781428462809917 3.266728289256198 1.2351171652892563 17.826982819746853 48.446694853956004 0.2915658337281323 48.622979422365624 18.36825048902798 2.086747875931804 6.973017956549777 4.285819698614021 1.44455802096413
user_002 Jogging 1.446034904692e12 -12.6451 -12.30022 -0.920286 -9.081314545454545 -6.775130363636364 -0.1573635454545455 159.89855400999997 151.29541204839998 0.8469263217960001 17.664679232304106 11.538779339376402 3.563785454545455 5.525089636363636 0.7629224545454545 6.725056115702478 3.691418768595042 1.289588834710744 12.700566766029754 30.526615489852855 0.5820506716496611 48.18622526434305 21.077174385491052 2.1372244049430615 6.94162987088357 4.59098838873407 1.4619248971623207
user_002 Jogging 1.446034904756e12 -14.06296 -10.57581 -0.690364 -14.644726363636364 -12.767033636363635 -0.25839 197.76684396160002 111.84775715610002 0.476602452496 17.609406678539628 19.475279502295486 0.5817663636363637 2.1912236363636346 0.43197399999999997 3.2382232727272733 4.010647933884297 0.4766286280991736 0.33845210185867775 4.8014610245586695 0.18660153667599996 12.985092626615568 20.309954201626507 0.2758041644698933 3.603483401739984 4.506656654508585 0.5251706051083718
user_002 Jogging 1.446034904846e12 -5.86241 0.881966 -2.22318 -8.245237272727273 -6.050527636363636 -2.007189090909091 34.3678510081 0.7778640251560001 4.9425293124000005 6.331527805013257 10.729046543855477 2.3828272727272735 6.9324936363636365 0.21599090909090934 4.018248099173554 3.8266475206611585 0.9485068347107437 5.677865811652896 48.05946801822232 4.665207280991746e-2 18.196603209903987 16.717464723465596 1.2873520720054776 4.265747673023334 4.08869963722766 1.134615385055869
user_002 Jogging 1.446034904854e12 -5.36425 3.14286 -2.52974 -7.541537272727272 -4.8382130909090915 -2.0594445454545456 28.7751780625 9.877568979600001 6.3995844675999995 6.712103359581109 9.819530956042142 2.1772872727272716 7.981073090909092 0.4702954545454543 4.071453140495868 4.330194743801655 0.856348520661157 4.74057986798016 63.69752768243321 0.22117781456611546 18.397149987892792 21.966001243425268 1.1072440962802064 4.289189898791238 4.686790078873308 1.0522566684417858
user_002 Jogging 1.446034904878e12 -2.10702 4.5224 -4.36911 -5.378180909090909 -1.4207367272727274 -2.4983863636363637 4.439533280399999 20.45210176 19.0891221921 6.631798943914087 7.87933259704107 3.271160909090909 5.943136727272727 1.8707236363636364 3.951109338842975 5.189711123966943 0.8959354462809919 10.70049369316446 35.320874159057986 3.4996069236495866 17.659479898589257 31.220205179266724 1.239098651056581 4.20231839567033 5.587504378456157 1.1131480813694918
user_002 Jogging 1.44603490491e12 -1.34061 -1.11069 0.497565 -3.281014181818182 1.4567706363636361 -2.52974 1.7972351721000002 1.2336322760999998 0.24757092922499999 1.810645845389153 5.179318167006664 1.940404181818182 2.567460636363636 3.0273049999999997 2.96301573553719 4.889799148760331 1.2677373636363638 3.7651683888174885 6.591854119276767 9.164575563024998 9.386070086848262 30.130454763090782 2.3786902039375906 3.0636693827579147 5.489121492833874 1.5423002962904437
user_002 Jogging 1.446034904918e12 -1.30229 -2.03038 1.53221 -2.9361314545454547 1.5821824545454546 -2.1813736363636362 1.6959592440999998 4.1224429444 2.3476674841 2.857633579135016 4.84376388010314 1.6338414545454547 3.6125624545454547 3.7135836363636363 2.677355123966942 4.804924578512398 1.5457965702479337 2.669437898591207 13.05060748799148 13.790703424267768 7.55499995359014 29.43798477202156 3.5933962969675406 2.748636016934607 5.425678277600097 1.8956255687681416
user_002 Jogging 1.446034905008e12 -14.40784 -15.86401 -3.67935 -4.430622363636363 -7.21407090909091 -0.7252006363636364 207.5858534656 251.6668132801 13.5376164225 21.74374124129056 8.775807580478014 9.977217636363637 8.64993909090909 2.9541493636363634 2.9516145867768597 5.136505157024794 1.7073115454545453 99.54487176336559 74.82144627643717 8.72699846267313 19.804817630125125 33.486343327562736 4.005089556027638 4.450260400260318 5.786738574323428 2.0012719845207543
user_002 Jogging 1.446034905072e12 -17.51178 -12.83671 -9.88724 -16.44578272727273 -14.568086363636365 -6.950508181818182 306.6624387684001 164.7811236241 97.75751481760001 23.857935309035025 23.287859721835208 1.065997272727273 1.7313763636363646 2.9367318181818183 7.326463776859504 4.696928760330579 3.9678935537190085 1.1363501854619842 2.997664112558681 8.624393771921488 63.111678662534125 30.135037766228844 17.07495917870299 7.94428591268807 5.489538939312558 4.132185762850333
user_002 Jogging 1.44603490508e12 -17.24354 -10.95901 -8.69931 -17.327149999999996 -14.338164545454545 -7.570600909090908 297.3396717316 120.09990018009998 75.67799447610001 22.20625061526146 23.891116942422705 8.360999999999663e-2 3.3791545454545453 1.1287090909090924 6.854269198347108 4.205415454545455 3.891886876033058 6.990632099999437e-3 11.418685442066115 1.27398421190083 60.58007336537378 24.155800296036443 16.839833156973718 7.7833202019044405 4.914855063583914 4.103636577107397
user_002 Jogging 1.446034905177e12 -5.47921 0.345482 2.2603 -12.596334545454546 -2.800267454545455 -3.7943080000000005 30.021742224100002 0.119357812324 5.1089560899999995 5.937175770214656 13.939753347675527 7.117124545454546 3.145749454545455 6.054608 3.6648138842975198 5.536176719008265 3.1514500495867774 50.653461795511575 9.895739630773027 36.658278033664 18.033930212002403 31.976428244376894 13.719504742224348 4.246637518319924 5.654770397140532 3.703984981371327
user_002 Jogging 1.446034905234e12 5.97857 1.83997 -2.6447 -1.852711727272727 1.1327883636363636 0.6821992727272729 35.7432992449 3.3854896009 6.994438089999999 6.791408317558296 6.010528689599741 7.831281727272727 0.7071816363636365 3.326899272727273 7.4420579008264465 2.783447090909091 4.073986148760331 61.32897349191571 0.5001058668099506 11.068258770873257 57.684432289683144 10.435126585578507 19.986831797446357 7.595026812966703 3.230344654302155 4.470663462781151
user_002 Jogging 1.446034905339e12 -5.47921 -3.52487 -4.59904 -1.4451221818181816 -3.3228180909090907 -1.2372999999999998 30.021742224100002 12.424708516899999 21.151168921599997 7.97481157536653 6.509562618986421 4.034087818181819 0.2020519090909092 3.3617399999999997 5.088052165289255 2.322020347107438 1.70287826446281 16.273864524802946 4.0824973967281034e-2 11.301295827599999 31.004942615609924 7.3387421684781975 4.120292266177289 5.568208205123971 2.70901128983956 2.0298503063470688
user_002 Jogging 1.446034905355e12 -5.63249 -2.95007 -4.75232 -3.6328649090909084 -3.6398318181818183 -2.097765272727273 31.724943600099998 8.702913004900001 22.5845453824 7.938035146520832 6.782794744382937 1.9996250909090914 0.6897618181818181 2.654554727272727 4.8840994710743795 2.0626460743801656 2.206426214876033 3.998500504193192 0.47577136582148755 7.0466608000859825 29.68185233968346 6.288701452326577 6.453050962520794 5.448105389920744 2.5077283450020214 2.540285606486167
user_002 Jogging 1.446034905469e12 -15.55745 -16.05561 1.76214 -14.630792727272727 -14.028118181818183 3.1346995454545454 242.0342505025 257.7826124721 3.1051373796000004 22.425922508432066 21.330167876689657 0.9266572727272724 2.0274918181818187 1.3725595454545454 4.255771157024793 3.5739233057851227 5.2546326280991735 0.8586937010983465 4.110723072794217 1.8839197058183883 27.394483158568065 25.26332975107513 37.281628055188655 5.233973935602666 5.026263995362274 6.105868329336022
user_002 Jogging 1.446034905615e12 -3.67815 2.60638 -1.64837 -6.73681 -4.211151818181819 -2.275431818181818 13.5287874225 6.793216704400001 2.7171236568999997 4.7999091432859435 8.70367169629476 3.05866 6.81753181818182 0.6270618181818182 4.062901983471075 4.511978595041322 1.0020288842975207 9.3554009956 46.47874009192151 0.3932065238214876 17.284075030573106 22.3073486315281 1.8275789039897425 4.157412059271141 4.72306559678437 1.3518797668393971
user_002 Jogging 1.446034905647e12 -1.30229 3.25783 -5.36544 -3.6154460909090913 -0.4418272727272725 -2.857203636363636 1.6959592440999998 10.613456308899998 28.787946393600006 6.410722420024127 6.502811399641062 2.313156090909091 3.699657272727272 2.5082363636363643 3.985945512396695 4.648158429752066 0.9136700826446282 5.3506911009098275 13.687463935643796 6.291249655867772 16.803033608622233 24.367466543648614 1.6582827814492298 4.099150352039095 4.936341412792334 1.2877432901977124
user_002 Jogging 1.446034905727e12 1.07357 -6.70546 1.53221 -1.8213575454545456 -2.3613268181818183 0.4035049090909093 1.1525525448999998 44.96319381160001 2.3476674841 6.96156690987022 5.193493556753845 2.8949275454545456 4.344133181818182 1.1287050909090908 1.1024227851239672 3.1625355371900823 3.0599230743801655 8.38060549343148 18.871493101373762 1.2739751822440988 1.9395304452291355 11.662849426100301 12.069842032723182 1.3926702571783227 3.4150914228026608 3.474167818733456
user_002 Jogging 1.446034905857e12 -17.20522 -5.82409 -6.20849 -18.59172 -10.513101818181818 -8.033929090909092 296.01959524840004 33.9200243281 38.545348080100005 19.195962274827487 22.924337680223914 1.386499999999998 4.689011818181818 1.825439090909092 6.054610214876033 2.2665984297520656 3.3813718842975207 1.9223822499999947 21.98683183104876 3.3322278746190124 54.52274721195797 7.463359831681368 14.768475546288306 7.383952004987435 2.7319150484012797 3.842977432445877
user_002 Jogging 1.446034905874e12 -14.21624 -3.90807 -6.55337 -18.072653636363636 -8.962870909090908 -8.103602727272728 202.10147973760002 15.2730111249 42.9466583569 16.134470837910985 21.862878092852522 3.8564136363636354 5.054800909090908 1.5502327272727277 4.436605834710743 2.4822690909090905 2.7169417520661163 14.871926134731398 25.551012230546267 2.403221508707439 30.28166977085812 9.45894439590338 10.4348205853482 5.50287831692271 3.0755396918107527 3.230297290552094
user_002 Jogging 1.446034906043e12 4.79064 -6.43721 -3.52607 5.079782727272728 -0.9121207272727273 -0.6102407272727273 22.9502316096 41.43767258410001 12.4331696449 8.764763193526681 6.032297508238341 0.2891427272727283 5.525089272727273 2.9158292727272723 4.460991074380165 2.1611383471074377 1.8317732727272726 8.360351673471132e-2 30.52661147160599 8.502060347693254 26.557241211071098 7.379857479956052 4.511605649858295 5.1533718293046835 2.7165893101379996 2.1240540600131377
user_002 Jogging 1.4460349061e12 -8.69811 -6.62882 -5.67201 -2.187142090909091 -5.298055454545454 -2.2510471818181816 75.65711757209999 43.9412545924 32.171697440100004 12.31949956794512 9.109836937026863 6.510967909090908 1.3307645454545458 3.4209628181818186 5.894360165289256 3.051057355371901 1.7427830826446282 42.39270311321163 1.770934275438844 11.70298660338249 49.28009459437685 11.136380565774072 4.236912042945018 7.019978247429036 3.337121598889389 2.0583760693675535
user_002 Jogging 1.446034906132e12 -15.74905 -17.81835 0.344284 -8.572700272727273 -9.20672818181818 -1.6274719090909096 248.0325759025 317.49359672249994 0.11853147265599999 23.78328623419514 13.337573546264622 7.1763497272727275 8.611621818181819 1.9717559090909096 7.523449586776858 4.22220267768595 1.909365611570248 51.49999540812735 74.16003033938513 3.8878213650349194 61.7491867891929 25.090541422510398 5.45840222276125 7.858065079215932 5.009045959313051 2.336322371326622
user_002 Jogging 1.446034906319e12 -3.56319 -4.98104 -1.22685 -7.67043181818182 -8.49606 -1.3487758181818184 12.696322976100001 24.8107594816 1.5051609225 6.2459781764108016 11.641939368073643 4.107241818181819 3.51502 0.12192581818181836 5.061765454545454 2.8714898347107436 0.936471603305785 16.869435353021498 12.355365600399999 1.4865905139305828e-2 26.613912440978808 10.115667026524044 1.3641360147597592 5.158867360281596 3.1805136419333344 1.1679623344781969
user_002 Jogging 1.446034906366e12 -3.60151 4.33079 -5.67201 -4.806859090909091 -2.3578414545454547 -2.4461315454545454 12.970874280100002 18.7557420241 32.171697440100004 7.993642082574126 7.147612929840088 1.205349090909091 6.688631454545455 3.225878454545455 2.907910165289256 5.069999818181818 0.8607821404958678 1.452866430955372 44.737790734734844 10.406291803500572 11.57677391751818 27.53574990156487 1.5309432143551114 3.402465858391261 5.247451753143126 1.237312900747063
user_002 Jogging 1.446034906408e12 -3.37159 -2.6435 -0.537083 -3.883689090909091 0.8924172727272727 -3.6201266363636364 11.3676191281 6.98809225 0.28845814888899995 4.3178894760043365 6.298627808131697 0.512099090909091 3.5359172727272727 3.0830436363636364 1.0894361157024794 4.591786280991736 1.653474876033058 0.26224547890991745 12.502710959571074 9.505158063722314 1.5332669035144262 25.43087279685827 3.9924662865406377 1.2382515509840584 5.042903211133273 1.9981156839734375
user_002 Jogging 1.446034906432e12 -0.919089 -6.01569 1.22565 -3.0406417272727277 -1.6819454545454447e-2 -2.571543909090909 0.8447245899210001 36.188526176100005 1.5022179224999999 6.207694313392132 6.2486913175856165 2.1215527272727277 5.998870545454546 3.797193909090909 1.1369413223140497 4.950603520661157 2.6136990661157027 4.500985974598349 35.98644782112212 14.418681583237097 1.6017300063233662 28.800501000828568 8.60560462482137 1.2655947243582228 5.366609823792723 2.9335310846864004
user_002 Jogging 1.44603490644e12 -0.535886 -4.44456 1.14901 -2.6713731818181823 -0.6369121818181819 -2.2440793636363634 0.287173804996 19.7541135936 1.3202239801000002 4.621851509806 6.148168785920205 2.135487181818182 3.807647818181818 3.3930893636363635 1.2534859421487603 4.643724429752066 2.887324933884298 4.560305503709762 14.49818190730476 11.513055429622222 1.950079957320572 25.427603458128235 9.638896397270868 1.3964526333966978 5.0425790482776005 3.1046572109124813
user_002 Jogging 1.446034906586e12 -18.73803 -8.77475 -10.577 -17.306248181818184 -11.910050909090911 -9.437845454545455 351.11376828089993 76.99623756249999 111.872929 23.237532890636217 23.287164041305836 1.4317818181818147 3.135300909090912 1.139154545454545 7.446174330578511 3.08209305785124 5.358825710743801 2.049999174876023 9.8301117905463 1.2976730784297512 71.52977110671443 15.74948846692201 31.739869182092267 8.457527481877575 3.968562518963511 5.633814798348652
user_002 Jogging 1.44603490665e12 -11.84038 -1.8771 -5.05888 -16.773248181818182 -5.029813636363637 -8.096633636363636 140.19459854439998 3.52350441 25.592266854400002 13.011931824629269 19.655628983671996 4.932868181818183 3.152713636363637 3.037753636363636 1.9451499999999997 4.46225694214876 2.7549444214876035 24.333188499194225 9.939603272913228 9.227947155240493 5.12739384022141 23.162048677438687 8.607126122767712 2.2643749336674373 4.812696611821556 2.933790401983024
user_002 Jogging 1.446034906675e12 -7.28026 -0.612526 -1.95493 -14.35906818181818 -2.295136727272727 -5.773029999999999 53.0021856676 0.375188100676 3.8217513049000003 7.563010318198436 15.684182358959674 7.0788081818181805 1.6826107272727273 3.8180999999999994 3.3383008264462815 4.440089074380165 2.8638885950413226 50.109525274976015 2.8311788595332565 14.577887609999996 16.27907204168686 22.948845464150352 8.696309456470926 4.034733205763035 4.790495325553544 2.9489505686720023
user_002 Jogging 1.446034906691e12 -3.56319 -0.267643 -0.575403 -11.673162727272727 -1.3859 -4.44575390909091 12.696322976100001 7.163277544900001e-2 0.331088612409 3.6192601956695514 12.589850897609228 8.109972727272726 1.1182569999999998 3.87035090909091 4.516729669421488 3.7012356942148754 3.247091570247935 65.77165763710742 1.2504987180489997 14.979616159500832 26.88297084850962 17.91654209487913 10.707331798979867 5.184879058233626 4.232793651346488 3.2722059530200522
user_002 Jogging 1.446034906796e12 3.48775 -1.8771 -0.15388 4.167061818181818 -0.2641598181818182 -1.0526639999999998 12.1644000625 3.52350441 2.3679054399999996e-2 3.9637839909485484 4.577401749353625 0.6793118181818176 1.6129401818181819 0.8987839999999998 4.622822 1.3089080247933884 0.7635545371900826 0.46146454632148687 2.6015760301236694 0.8078126786559997 29.624850336280137 2.1701642934302128 0.9407629565015231 5.4428715156872975 1.473147750033992 0.9699293564489752
user_002 Jogging 1.446034906901e12 -13.90968 -14.82936 0.574206 -9.422715454545456 -7.56243818181818 -1.536894909090909 193.4791977024 219.90991800959998 0.329712530436 20.34007935683723 12.62745717317743 4.486964545454544 7.266921818181819 2.1111009090909088 5.990321280991736 3.6831837438016533 1.5651153305785124 20.132850832166103 52.808152711566954 4.456747048364462 39.355071607140815 25.29494812891069 3.5116251320448835 6.27336206568223 5.029408327915988 1.8739330649852153
user_002 Jogging 1.446034906942e12 -13.25823 -11.95534 0.957409 -12.234032727272727 -11.370082727272724 6.907418181818191e-2 175.78066273289997 142.93015451559998 0.9166319932809999 17.878127677186473 17.14557775538888 1.0241972727272728 0.5852572727272758 0.888334818181818 3.535603636363635 4.105973884297521 2.7207424297520664 1.0489800534619835 0.3425260752801689 0.7891387491941237 14.143379773647181 27.92088436341037 9.16121299243646 3.7607685083832507 5.284021608908348 3.0267495754416913
user_002 Jogging 1.446034906982e12 -18.20155 -13.83303 0.114362 -14.93735727272727 -13.091012727272728 0.9992131818181815 331.2964224025 191.35271898090002 1.3078667044000002e-2 22.861807016297817 20.021431218737533 3.2641927272727305 0.7420172727272725 0.8848511818181816 2.679888842975208 2.0886154545454545 2.392960826446281 10.654954160780186 0.5505896330256195 0.7829616139650326 8.170243855914727 9.0530824903009 7.778509837779979 2.8583638424656033 3.008834074903583 2.7889979988841835
user_002 Jogging 1.446034907039e12 -10.49917 -9.08132 -1.49509 -15.240435454545455 -12.43260181818182 -0.7321686363636364 110.23257068889998 82.47037294239999 2.2352941081 13.962028424960321 19.741622779485223 4.741265454545456 3.3512818181818194 0.7629213636363636 2.6935066115702493 1.4317850413223132 1.1613272644628099 22.479598110466128 11.23108982487604 0.5820490070927686 8.706909684042682 2.7814735594394424 2.090464572249903 2.9507473094188668 1.6677750326226384 1.4458438962245899
user_002 Jogging 1.44603490712e12 -3.06503 0.11556 -2.33814 -5.9286 -3.9533609090909083 -2.0037054545454542 9.3944089009 1.3354113599999998e-2 5.466898659600001 3.8567682940643455 7.647626963046448 2.86357 4.068920909090909 0.3344345454545459 4.9176669421487595 4.3891014049586765 0.6926151570247933 8.2000331449 16.556117364437185 0.11184646519338871 24.717201992823444 19.88589517058234 0.711008412292816 4.971639769012176 4.459360399270543 0.8432131476043385
user_002 Jogging 1.446034907209e12 -2.60518 -2.91174 -0.268841 -3.305399090909091 1.7563679090909092 -2.2754321818181817 6.7869628323999995 8.4782298276 7.2275483281e-2 3.9163079734976156 5.2126819531418045 0.7002190909090911 4.668107909090909 2.0065911818181816 0.514316033057851 3.7867450082644627 1.3510296363636363 0.49030677527355393 21.7912314509171 4.026408170950487 0.3872811612404208 17.10425060984232 2.3858715021441905 0.6223191795537245 4.135728546440436 1.5446266546140497
user_002 Jogging 1.446034907257e12 -4.9044 -8.73643 -1.03525 -3.1416672727272728 -3.047608181818182 -0.3977359999999999 24.05313936 76.3252091449 1.0717425625 10.072243596508178 5.088328791502168 1.7627327272727271 5.688821818181818 0.6375140000000001 0.4753612396694214 3.6527827355371896 1.6974960330578512 3.1072266677983467 32.36269367902149 0.40642410019600017 0.44791110553456037 15.898295291524887 4.0854805384043535 0.6692616121776 3.9872666441467 2.0212571678053126
user_002 Jogging 1.44603490729e12 -14.5228 -14.98264 -4.714 -6.297867272727273 -8.168595454545455 -1.5194766363636367 210.91171984000002 224.4795013696 22.221796000000005 21.39189138925308 10.659201066417712 8.224932727272726 6.814044545454545 3.194523363636364 2.833486611570248 5.709093859504131 2.4113304049586772 67.64951836816196 46.43120306743883 10.204979520818588 18.223977300926823 37.259208245871726 7.400173065667297 4.268955059604964 6.104032130147393 2.720325911663398
user_002 Jogging 1.446034907403e12 -16.32385 -2.98839 -6.70665 -18.60913818181818 -8.074537272727271 -9.162636363636363 266.4680788225 8.9304747921 44.9791542225 17.899097961548232 22.465247190479637 2.2852881818181814 5.086147272727271 2.455986363636363 2.8151174380165287 3.4934809917355367 3.2850951074380172 5.222542073957849 25.868894079871062 6.0318690183677655 13.08808219941119 15.12350955082885 15.124677542114034 3.6177454580734656 3.888895672402237 3.8890458395490834
user_002 Jogging 1.446034907476e12 -1.72382 -1.8771 7.6042e-2 -9.690955454545456 -2.1314045454545454 -3.644511272727273 2.9715553923999996 3.52350441 5.782385764e-3 2.5496749181344667 10.660497360124252 7.967135454545456 0.2543045454545454 3.720553272727273 5.511791239669422 2.9132938842975205 2.9712496363636363 63.47524735107523 6.467080183884294e-2 13.842516655201623 34.147024354188275 11.661232288066863 9.041529347618932 5.843545529401502 3.4148546510893936 3.0069135916449166
user_002 Jogging 1.446034907508e12 3.90927 -1.18733 0.689167 -3.514419181818181 -1.7725872727272725 -0.8575789090909095 15.282391932899998 1.4097525289 0.47495115388899994 4.143319395809234 5.937236157961707 7.423689181818181 0.5852572727272725 1.5467459090909095 7.149745727272728 1.24398520661157 3.22270714876033 55.1111610682443 0.342526075280165 2.392422907289464 51.78529644795792 2.918500147336363 10.751469564364557 7.196200139515153 1.708361831503023 3.278943360957087
user_002 Jogging 1.44603490771e12 -17.43514 -8.54483 3.48655 -12.686907272727273 -8.206916181818181 0.26067618181818175 303.9841068196 73.01411972889998 12.1560309025 19.726993117325307 16.107928203941583 4.748232727272727 0.33791381818181776 3.2258738181818183 5.759764016528926 4.008115272727272 3.1717190578512398 22.5457140323438 0.11418574851821459 10.406261890830942 35.661160756290485 30.533416247583013 12.024295610812407 5.971696639673727 5.525705045293588 3.4676066113116706
user_002 Jogging 1.446034907759e12 -15.86401 -13.71807 0.919089 -16.041677272727274 -11.666194545454545 0.6682655454545454 251.6668132801 188.18544452490002 0.8447245899210001 20.992784055358666 20.2523659330661 0.17766727272727323 2.0518754545454563 0.2508234545454546 3.3924554545454555 2.6558187107438016 2.6403015702479338 3.156565979834729e-2 4.210192880966122 6.291240535011575e-2 22.638652582823227 12.02031983930934 9.005259334218808 4.758009308820573 3.4670332907702717 3.000876427682221
user_002 Jogging 1.446034907783e12 -15.05928 -13.10495 -0.767005 -15.623637272727272 -12.209646363636363 -3.1951000000000035e-2 226.78191411839998 171.73971450250002 0.5882966700250001 19.977735739841116 20.14726044376983 0.564357272727273 0.8953036363636375 0.735054 1.4406541322314053 1.6569572396694214 1.9061984958677685 0.3184991312801656 0.8015686012859525 0.540304382916 6.287423676600526 3.4110413267804827 6.197944188322239 2.5074735644868773 1.8469004647734764 2.489567068452312
user_002 Jogging 1.446034907864e12 -6.20729 -2.56686 -1.38013 -12.718260000000003 -7.496248181818183 -2.3799413636363638 38.530449144100004 6.5887702596 1.9047588169000003 6.857403168882518 15.077684317245383 6.510970000000002 4.929388181818183 0.9998113636363637 2.894924958677685 3.608126859504132 1.1971138512396695 42.39273034090003 24.298867847048772 0.9996227628564051 11.904642383664612 15.582626079113071 2.082914951146491 3.4503104764157984 3.947483512202815 1.443230733855987
user_002 Jogging 1.446034907913e12 -3.63983 4.56072 -1.34181 -6.733325454545454 -1.8318086363636361 -2.6168318181818178 13.248362428899998 20.8001669184 1.8004540760999999 5.987402059608157 8.085788948485714 3.0934954545454545 6.392528636363636 1.2750218181818178 4.686161570247934 4.656393636363637 1.0932376942148758 9.569714127293388 40.86442236672913 1.6256806368396686 23.52487566892179 22.36712104831754 1.8064930810530717 4.850244908138329 4.729389077705231 1.3440584366213664
user_002 Jogging 1.446034907945e12 -3.06503 1.49509 -3.25783 -4.030002727272727 1.2512349999999997 -2.5645781818181814 9.3944089009 2.2352941081 10.613456308899998 4.7162653994341746 5.7354608954132 0.9649727272727269 0.24385500000000038 0.6932518181818184 3.8944193388429755 4.135427107438016 0.7736911983471072 0.9311723643801645 5.946526102500018e-2 0.48059808341239696 18.571364015563038 19.700650420240237 0.7393206976024979 4.309450546828799 4.438541474430563 0.8598375995515071
user_002 Jogging 1.446034908139e12 -16.20889 -13.83303 -8.43107 -5.102970909090908 -0.7588395454545455 -2.811918363636364 262.7281150321 191.35271898090002 71.08294134490001 22.91645206741 7.7895724282934165 11.105919090909092 13.074190454545455 5.619151636363636 3.8196790082644627 5.613133347107438 1.4023345041322315 123.34143885381903 170.9344560417275 31.57486511244813 22.279454978312547 45.07532239502894 3.9238172475664856 4.720111754854174 6.713815785008473 1.9808627533391823
user_002 Jogging 1.446034908269e12 -3.67815 -1.76214 1.49389 -5.963435454545455 -1.3824163636363638 -2.7317938181818175 13.5287874225 3.1051373796000004 2.2317073320999996 4.343458545237885 8.741728257991952 2.285285454545455 0.37972363636363626 4.2256838181818175 3.8149286776859497 5.324939834710744 1.9438858264462808 5.222529608757027 0.14419004001322305 17.856403731243663 23.576699133056877 43.9105411464654 6.288745057720135 4.855584324574837 6.626502934917134 2.5077370391889446
user_002 Jogging 1.446034909046e12 -5.05768 -1.07237 -0.652044 -9.196275454545455 -6.566109090909092 -2.5088374545454544 25.580126982400003 1.1499774169 0.42516137793599995 5.211071461536101 12.418203811353674 4.138595454545455 5.4937390909090915 1.8567934545454543 4.934770495867768 5.036428710743802 2.7267596363636364 17.127972336384303 30.18116919898265 3.447681932842842 33.161018213630584 33.540879014491985 12.511976010260918 5.758560428929315 5.791448783723464 3.5372271640737067
user_002 Jogging 1.446034910211e12 -17.05194 -11.6871 -1.07357 -8.475157272727273 -6.458115909090908 -2.310269909090909 290.76865776359995 136.58830640999997 1.1525525448999998 20.700471412953377 11.878641916477324 8.576782727272725 5.228984090909091 1.2366999090909092 6.322850239669421 4.93666985123967 2.176657570247934 73.56120195084377 27.342274622980376 1.529426665145463 49.513870967071085 29.244915742785835 8.943415354326099 7.036609337391915 5.407856853022816 2.9905543556882725
user_002 Jogging 1.446034910923e12 -15.0976 -14.3312 1.49389 -7.0747254545454545 -5.541911818181819 -2.4983859090909095 227.93752576 205.38329344000002 2.2317073320999996 20.869895220918096 10.728633853208215 8.022874545454545 8.789288181818183 3.9922759090909095 5.452567685950413 4.862880545454545 2.2213097768595045 64.36651597210248 77.25158674304878 15.938266934307647 42.44141270557199 30.712884458420195 8.367651130529412 6.514707415193103 5.541920647069948 2.892689255784211
user_002 Jogging 1.446034911246e12 -4.75112 -6.39889 0.880768 -6.914476363636364 -5.099486363636363 -1.561280909090909 22.573141254400003 40.945793232099994 0.775752269824 8.01839676969929 10.070073140367551 2.163356363636364 1.2994036363636363 2.442048909090909 5.080449173553718 4.724800603305787 1.8662933884297523 4.680110756085952 1.6884498101950411 5.963602874392099 38.99996173856371 29.48726106588099 4.7361626274450375 6.24499493503107 5.430217405029102 2.1762726454755246
user_002 Jogging 1.446034912153e12 -17.93331 -10.53749 -8.23947 -8.192980727272726 -5.761382454545454 -2.0733795454545456 321.6036075561 111.0386955001 67.88886588090001 22.372553920755227 11.34494337446847 9.740329272727273 4.776107545454546 6.166090454545455 6.011221520661156 4.700413702479339 1.9571871735537187 94.8740143411478 22.811203285747848 38.02067149363658 47.0317002322874 26.73019534190938 7.5335368870795465 6.857966187747458 5.170125273328431 2.744728927795885
user_002 Jogging 1.44603491306e12 -0.957409 1.91661 1.22565 -9.095248999999999 -6.325737 -2.470517545454545 0.9166319932809999 3.6733938920999996 1.5022179224999999 2.468247112402038 12.124781946634904 8.137839999999999 8.242347 3.696167545454545 4.828992140495868 4.8625619173553725 2.2665985950413226 66.22443986559998 67.93628406840901 13.661654524071476 33.47302909714301 31.912813317328006 7.9159821938666335 5.785588051109672 5.649142706404929 2.8135355327179776
user_002 Jogging 1.446034913319e12 -17.85667 -13.41151 -1.80165 -8.078019 -5.792736090909091 -2.1569866363636367 318.86066348890006 179.86860048009999 3.2459427224999997 22.404803205819505 11.181984622796081 9.778651000000002 7.618773909090909 0.35533663636363677 5.500073561983472 5.078866123966942 2.0981160330578517 95.62201537980104 58.04571587784437 0.12626412514222343 41.78944792661123 32.60139135892502 6.684421911083967 6.464475843145462 5.7097628110916325 2.5854248995250213
user_002 Jogging 1.446034914095e12 -17.39682 -10.49917 -3.56439 -8.861844909090909 -5.841506090909092 -1.6866936363636365 302.64934611240005 110.23257068889998 12.7048760721 20.629755036679423 11.92481561399505 8.534975090909093 4.657663909090908 1.8776963636363635 5.914944851239668 4.972139173553718 2.1484710495867767 72.84579980243868 21.693833090047995 3.5257436340132227 46.87801609592091 27.69571628107252 7.233094173347625 6.846752229774414 5.262671971638791 2.6894412381287727
user_002 Jogging 1.446034914937e12 -14.06296 -9.31124 0.344284 -10.049774545454547 -6.2351611818181825 -0.21658654545454545 197.76684396160002 86.6991903376 0.11853147265599999 16.86963442911126 12.252088776393206 4.013185454545454 3.0760788181818173 0.5608705454545454 5.10451879338843 4.026167958677686 2.1858417190082644 16.1056574925752 9.462260895666846 0.31457576875847926 34.7920575256337 20.439095374185214 5.985408657440974 5.898479255336388 4.520961775351038 2.446509484437159
user_002 Jogging 1.446034915132e12 -3.79311 -0.650847 0.267643 -8.910616363636363 -4.698865454545454 -0.1364618181818182 14.387683472099999 0.42360181740899994 7.163277544900001e-2 3.8578385224057783 10.584969232379587 5.1175063636363625 4.0480184545454545 0.4041048181818182 4.847044462809918 4.361232578512396 1.7323318264462806 26.188871381858664 16.38645340834057 0.16330070407776034 29.31876509625081 22.243799336105926 4.342268189926172 5.4146805165448875 4.716333251171499 2.083810977494401
user_002 Jogging 1.446034915455e12 1.76333 -1.72382 -0.345482 -10.067193636363639 -5.4374033636363635 -1.052664909090909 3.1093326889000004 2.9715553923999996 0.119357812324 2.4900292957360963 12.23813765690058 11.830523636363639 3.7135833636363635 0.707182909090909 5.288836694214875 4.095206685950413 1.8130885867768596 139.96128951055874 13.790701398676768 0.5001076669102809 38.75524016966116 20.051422205409718 7.297770799175755 6.22537068532157 4.4778814416428805 2.701438653602142
user_002 Jumping 1.446035033798e12 -1.30229 0.307161 -0.920286 -6.6575549999999994 -7.12697975 0.7179085000000001 1.6959592440999998 9.434787992100001e-2 0.8469263217960001 1.6239561095722383 10.874421038711667 5.355264999999999 7.43414075 1.6381945 3.794508333333333 5.764013104166667 1.3292357083333333 28.678863220224994 55.26644869081056 2.68368121983025 20.47484638194861 50.37997567727834 2.93820038376659 4.524913964038278 7.0978852961483065 1.7141179608669266
user_002 Jumping 1.446035033863e12 -0.344284 -0.535886 0.344284 -5.394900799999999 -5.8087610000000005 0.6431836000000001 0.11853147265599999 0.287173804996 0.11853147265599999 0.7240419534170655 8.844345221652747 5.050616799999999 5.272875000000001 0.2988996000000001 4.045730026666666 5.665785483333333 1.1231684866666667 25.508730060442232 27.80321076562501 8.934097088016006e-2 21.481623117647334 45.86462269494767 2.368428501189304 4.634827193935857 6.7723424821067395 1.5389699481111723
user_002 Jumping 1.446035033927e12 -0.842448 -0.650847 -1.26517 -4.636158666666666 -4.949108666666667 0.32512466666666673 0.7097186327039999 0.42360181740899994 1.6006551288999997 1.6534737914502906 7.645866649952336 3.793710666666666 4.298261666666667 1.5902946666666666 4.0037268 5.437864847222222 1.20102285 14.392240622380438 18.47505335513611 2.5290371268284444 20.300059368436184 41.29969447164574 2.3951966054624942 4.505558718786848 6.42648383423204 1.5476422730923622
user_002 Jumping 1.446035034057e12 -6.70546 -11.6871 0.842448 -4.46850775 -5.37382525 0.20537325000000006 44.96319381160001 136.58830640999997 0.7097186327039999 13.50041550672808 7.712791209417686 2.2369522500000008 6.313274749999999 0.6370747499999999 3.647825774107143 5.225442836309524 1.1384730526785716 5.0039553687800655 39.85743806898755 0.40586423708756236 16.918744297309093 36.98160318013506 2.0470234572839994 4.11324012152331 6.081250133001854 1.4307422749342382
user_002 Jumping 1.446035034445e12 -1.76214 -0.267643 -0.996927 -4.765055181818181 -5.541912 0.2571926363636364 3.1051373796000004 7.163277544900001e-2 0.993863443329 2.042212917004003 7.982462080994015 3.002915181818181 5.274269 1.2541196363636364 4.781736989249638 6.35182557994228 1.1887342204971796 9.01749958919412 27.817913484361004 1.5728160623128598 31.74650931534741 50.318931638349575 2.334578496357329 5.634404078103327 7.093583835999231 1.5279327525638453
user_002 Jumping 1.446035035417e12 -7.1653 -11.61046 0.995729 -6.830867636363636 -8.680694454545455 0.25719163636363634 51.34152409 134.8027814116 0.991476241441 13.679758102504628 11.519586108272428 0.3344323636363642 2.9297655454545453 0.7385373636363637 4.467641735537191 6.43369567768595 0.7800238347107438 0.11184500584740534 8.583526151332569 0.5454374374869505 27.654650666007146 49.408091581727376 0.6421275864403456 5.258768930653556 7.029088958160038 0.801328638225507
user_002 Jumping 1.446035036064e12 0.652044 0.881966 -1.11189 -5.5419113636363635 -6.719390818181818 -0.2897428181818182 0.42516137793599995 0.7778640251560001 1.2362993721000002 1.561833786032304 9.428134272108517 6.193955363636364 7.601356818181818 0.8221471818181818 4.442940404958677 6.1952234876033065 0.7898413223140497 38.365083046719676 57.78062547731922 0.6759259885715785 26.80721992624767 44.61862266479724 0.8233101719703998 5.177568920472973 6.6797172593454315 0.907364409689073
user_002 Jumping 1.446035036647e12 -0.459245 -2.14534 -0.767005 -5.242317090909091 -6.984149545454546 4.4688181818181814e-2 0.210905970025 4.6024837156 0.5882966700250001 2.3241528253645454 9.347194647011769 4.783072090909091 4.838809545454546 0.8116931818181818 4.471758231404959 6.197756520661158 0.8256284876033056 22.87777862683346 23.41407781718203 0.658845821410124 24.532754052048745 43.48848930565539 0.8307662014794853 4.9530550221099645 6.594580297915508 0.9114637686049212
user_002 Jumping 1.446035041373e12 -0.535886 -1.64717 -1.03525 -5.538427636363636 -7.430056727272727 -0.43257309090909096 0.287173804996 2.7131690089 1.0717425625 2.0179408753469463 9.764384790075978 5.002541636363636 5.782886727272727 0.602676909090909 4.196865702479339 5.813602826446282 0.5177994214876033 25.025422823551768 33.44177890046707 0.3632194567513718 22.45182419827358 38.34933714049541 0.40652773556788735 4.73833559367354 6.1926841628243405 0.6375952756787705
user_002 Jumping 1.44603504215e12 -1.14901 -0.420925 0.344284 -4.291276363636364 -6.2247104545454555 -0.509214 1.3202239801000002 0.177177855625 0.11853147265599999 1.2711936549483718 8.034460275069408 3.1422663636363644 5.803785454545455 0.8534980000000001 3.6496149421487605 6.059993644628099 0.4718784380165289 9.8738379000405 33.683925602393394 0.7284588360040002 18.037898103949008 41.549573370724985 0.2748523318328828 4.247104673062463 6.445895854784266 0.5242636091060325
user_002 Jumping 1.446035042538e12 -3.63983 -3.86975 -0.383802 -5.1761277272727275 -8.179046636363637 -0.6102402727272728 13.248362428899998 14.974965062499999 0.147303975204 5.326408871519722 10.024669937451387 1.5362977272727276 4.309296636363637 0.2264382727272728 3.2996652644628095 5.7654663553719 0.49024671900826444 2.360210706823348 18.57003750017496 5.127429135571077e-2 14.209285799045562 38.99125749161312 0.2935062012731563 3.769520632526842 6.244297998303182 0.5417621260970135
user_002 Jumping 1.446035042926e12 -10.11596 -15.25089 3.7722e-2 -5.256252272727273 -8.29400809090909 -0.7739722727272725 102.33264672159999 232.58964579210001 1.422949284e-3 18.30092116432897 10.515775377709382 4.859707727272727 6.95688190909091 0.8116942727272726 3.8808028760330573 6.340904438016529 0.6865985123966943 23.616759194514252 48.39820589703639 0.658847592378256 18.500328994784294 45.368967180312275 0.8470754660074215 4.301200878218117 6.735648979891416 0.9203670278793246
user_002 Jumping 1.446035043445e12 -3.56319 -7.81675 -1.34181 -4.611774090909091 -6.754229181818181 -1.1641431818181818 12.696322976100001 61.1015805625 1.8004540760999999 8.694731601073146 8.943475057995606 1.0485840909090913 1.0625208181818184 0.17766681818181818 3.6682985206611574 5.689458504132232 0.9301391074380163 1.0995285957076455 1.1289504890697608 3.156549828285124e-2 18.615440480806782 42.168303896146696 1.3272466957004447 4.314561447100595 6.493712643484211 1.1520619322330048
user_002 Jumping 1.446035043574e12 -14.36952 -19.58108 0.420925 -6.416312272727273 -9.380912181818182 -0.700815909090909 206.4831050304 383.4186939664 0.177177855625 24.29154126136143 12.046808337182016 7.953207727272726 10.200167818181818 1.121740909090909 4.3900503388429755 6.595844338842975 1.0026629669421487 63.25351315315061 104.04342351907202 1.2583026671280988 25.11940654493285 53.231245821914314 1.426878065493329 5.011926430518793 7.29597463139191 1.1945200146893016
user_002 Jumping 1.446035043638e12 -13.64143 -17.24354 -0.1922 -6.736809545454546 -9.562062181818181 -0.721717909090909 186.08861244489998 297.3396717316 3.694084e-2 21.987842663992755 12.381983018969633 6.904620454545454 7.681477818181818 0.529517909090909 4.575951495867769 6.661716694214875 0.9770105702479338 47.67378362132747 59.00510147121931 0.2803892160480082 27.306408765552234 54.19550905593094 1.3924727585542156 5.225553441077054 7.3617599156676485 1.1800308294931179
user_002 Jumping 1.446035044156e12 -15.21256 -18.89131 -0.767005 -6.479016363636364 -8.948935545454544 -0.46740981818181826 231.4219817536 356.88159351610005 0.5882966700250001 24.267094427222332 11.74871380185931 8.733543636363635 9.942374454545456 0.2995951818181818 4.947753066115702 6.188887628099172 0.6663303057851239 76.27478444826774 98.85080979439806 8.975727296866941e-2 29.89711555340259 46.60736227828711 0.736107912280498 5.467825486736257 6.826958493962528 0.8579673142261878
user_002 Jumping 1.446035044545e12 -0.650847 -0.420925 0.267643 -5.46527009090909 -6.9109917272727275 -0.8715146363636365 0.42360181740899994 0.177177855625 7.163277544900001e-2 0.8200075905032831 9.38892652315506 4.814423090909091 6.490066727272728 1.1391576363636364 4.232968380165289 6.326651388429752 0.7106671983471075 23.178669698278643 42.120966124452536 1.297680120485587 23.35106811043113 46.895908393370604 0.6108041687504081 4.8322942905447235 6.848058731740741 0.7815396143193306
user_002 Jumping 1.446035045839e12 -8.85139 -11.72542 0.305964 -5.521009272727273 -7.200136545454544 -0.35593254545454545 78.34710493210001 137.4854741764 9.361396929600001e-2 14.694427279679736 9.497917300387376 3.3303807272727273 4.525283454545455 0.6618965454545455 4.381500247933884 5.622635090909091 0.6425778429752066 11.091435788589619 20.478190343982853 0.4381070368846612 23.812058545512084 36.44259565766158 0.590843696719027 4.879760090979072 6.036770300223587 0.7686635783741982
user_002 Jumping 1.446035046098e12 -3.29495 -4.59784 -0.15388 -5.844989272727273 -8.078020181818182 -0.742618909090909 10.856695502500001 21.140132665599996 2.3679054399999996e-2 5.658666558695608 10.38518386833713 2.5500392727272727 3.4801801818181826 0.588738909090909 4.263687842975207 5.87725885123967 0.7185846528925619 6.502700292451438 12.111654097920038 0.34661350307755356 20.709634302860838 39.065419343592374 0.8314188977151395 4.550783921794226 6.250233543123999 0.911821746678121
user_002 Jumping 1.44603504681e12 -0.229323 0.1922 -0.498763 -4.876532181818181 -6.099299000000001 -0.7286840000000001 5.2589038329e-2 3.694084e-2 0.24876453016900002 0.5816308180435421 8.625721908563833 4.647209181818181 6.291499000000001 0.2299210000000001 4.378649504132231 6.121748975206612 0.6143912975206612 21.59655317957521 39.58295966700101 5.286366624100004e-2 23.7805886174197 45.32605095967318 0.49155458285741943 4.876534488488695 6.732462473692162 0.7011095369893491
user_002 Jumping 1.446035046875e12 -0.842448 -1.03405 -0.345482 -4.9636238181818175 -6.322253545454546 -0.6276578181818181 0.7097186327039999 1.0692594024999997 0.119357812324 1.377801091423577 8.565835701140356 4.121175818181817 5.288203545454547 0.2821758181818181 4.2956750578512395 5.860474157024794 0.5868385950413223 16.984090124366574 27.96509673815804 7.962319236657846e-2 23.020951708247193 41.81179228320366 0.4676545045738995 4.798015392664679 6.466203854132938 0.6838526921595758
user_002 Jumping 1.446035047198e12 -13.21991 -16.63042 0.420925 -7.189685909090908 -9.31123781818182 -0.6172074545454546 174.76602040810002 276.5708693764 0.177177855625 21.248860384503566 12.192681806384348 6.030224090909092 7.3191821818181815 1.0381324545454547 5.007293181818182 6.497985338842975 0.5244497603305786 36.36360258658039 53.57042781064475 1.0777189931805704 30.092576058743646 51.20800629042747 0.39204926912054333 5.485670064699812 7.1559769626814385 0.6261383785718164
user_002 Jumping 1.446035047263e12 -4.06135 -5.90073 -3.8919e-2 -6.175939545454545 -8.067569636363636 -0.5301158181818182 16.4945638225 34.8186145329 1.514688561e-3 7.163427464835601 10.58790123368122 2.114589545454545 2.166839636363636 0.4911968181818182 4.47302656198347 5.761348685950413 0.5618199338842975 4.4714889457456595 4.695194009716494 0.24127431419194217 24.693217809402338 42.04669015402345 0.4133996612237356 4.969227083702489 6.48434192143069 0.6429616327773654
user_002 Jumping 1.446035048687e12 -0.650847 -1.95374 7.6042e-2 -4.9009182727272735 -6.210774181818182 -0.8749986363636364 0.42360181740899994 3.8170999876000002 5.782385764e-3 2.0606999273967572 8.582328109355055 4.250071272727274 4.257034181818182 0.9510406363636363 4.732400454545455 5.830070454545455 0.7724236694214877 18.06310582326163 18.1223400251684 0.9044782920149503 26.822176913056808 41.98446965925354 0.8800927343924942 5.179013121537423 6.479542395821911 0.938132578259861
user_002 Jumping 1.446035049399e12 -13.52647 -17.3585 -0.958607 -6.747261090909091 -8.673726545454544 -0.18174954545454547 182.9653906609 301.31752224999997 0.918927380449 22.027297616624445 11.464334396480872 6.779208909090909 8.684773454545455 0.7768574545454545 4.79384026446281 5.962767528925619 0.6299100495867768 45.957673433097554 75.4252899567774 0.603507504682843 28.706956726588132 41.384309342338895 0.6132338470071389 5.357887337989492 6.433063760164273 0.7830924894334889
user_002 Jumping 1.4460350505e12 -6.24561 -9.73276 -1.15021 -5.1587095454545455 -7.287228 -0.35593318181818184 39.0076442721 94.72661721760001 1.3229830441 11.62141319004707 9.455231050646873 1.0869004545454546 2.445532000000001 0.7942768181818181 3.9922799008264462 5.749314008264463 0.5428184214876032 1.1813525980911157 5.980626763024005 0.6308756639010329 21.690321855347296 39.86753617989176 0.427005639025329 4.657286962958938 6.314074451563884 0.6534566848883934
user_002 Sitting 1.44603456478e12 -0.765807 -0.114362 9.4262 -0.765807 -0.114362 9.4262 0.586460361249 1.3078667044000002e-2 88.85324643999999 9.457948269487044 9.457948269487044 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
user_002 Sitting 1.446034565169e12 -0.842448 -0.152682 9.4262 -0.8205507142857142 -0.1417335714285714 9.431674285714285 0.7097186327039999 2.3311793124000002e-2 88.85324643999999 9.465002739874299 9.468437272237447 2.189728571428573e-2 1.0948428571428609e-2 5.474285714285543e-3 1.6631628911564604e-2 1.86257112244898e-2 1.611004081632699e-2 4.7949112165306194e-4 1.198680881836743e-4 2.9967804081630776e-5 5.057707334111117e-4 4.989748108321516e-4 4.2989052794818944e-4 2.2489347109489678e-2 2.233774408556405e-2 2.073380157974387e-2
user_002 Sitting 1.446034565688e12 -0.919089 -3.7722e-2 9.4262 -0.8389643636363636 -0.13874772727272727 9.433167272727273 0.8447245899210001 1.422949284e-3 88.85324643999999 9.470976400519906 9.471666852469868 8.012463636363643e-2 0.10102572727272727 6.967272727273155e-3 3.203548127705629e-2 4.438884426406926e-2 1.5275845992391623e-2 6.4199573524049685e-3 1.020619757098347e-2 4.854288925620431e-5 1.4169701993062044e-3 2.985819414044147e-3 4.3332102977010464e-4 3.764266461485165e-2 5.464265196752576e-2 2.0816364470533866e-2
user_002 Sitting 1.446034566658e12 -0.727487 -0.305964 9.54116 -0.7692910909090908 -0.16313345454545455 9.447101818181817 0.529237335169 9.361396929600001e-2 91.0337341456 9.57374458872102 9.480753256798605 4.180409090909076e-2 0.14283054545454546 9.405818181818226e-2 8.455804958677687e-2 5.732220661157026e-2 4.180363636363635e-2 1.747582016735525e-3 2.0400564714842976e-2 8.846941566942232e-3 1.325132104521563e-2 4.601724310780617e-3 2.299167754770868e-3 0.1151143824429234 6.783601042794761e-2 4.794963769175809e-2
user_002 Sitting 1.446034567047e12 -0.765807 -0.152682 9.46452 -0.7832257272727273 -0.18055172727272728 9.436650909090906 0.586460361249 2.3311793124000002e-2 89.5771388304 9.496678945019307 9.471570465860033 1.7418727272727308e-2 2.7869727272727268e-2 2.7869090909094396e-2 5.763904132231403e-2 6.555633884297521e-2 3.705322314049604e-2 3.034120598016541e-4 7.76721698256198e-4 7.766862280993679e-4 7.076373251673926e-3 6.461827551674681e-3 1.9317863429000853e-3 8.412118194410921e-2 8.03854934156324e-2 4.3952091450806814e-2
user_002 Sitting 1.446034567111e12 -0.765807 -0.114362 9.4262 -0.75884 -0.1666170909090909 9.436650909090908 0.586460361249 1.3078667044000002e-2 88.85324643999999 9.457948269487044 9.468972667311892 6.9670000000000565e-3 5.2255090909090904e-2 1.0450909090907956e-2 3.578690082644627e-2 6.143933057851239e-2 3.546975206611594e-2 4.853908900000079e-5 2.7305945259173546e-3 1.0922150082642256e-4 1.519209039241922e-3 5.845110941862509e-3 1.8711077313298444e-3 3.897703220156612e-2 7.645332525052465e-2 4.325630279311726e-2
user_002 Sitting 1.446034567241e12 -0.804128 -0.114362 9.46452 -0.7727747272727273 -0.17358445454545457 9.440134545454544 0.646621840384 1.3078667044000002e-2 89.5771388304 9.499307308316117 9.473686950322545 3.135327272727262e-2 5.9222454545454564e-2 2.4385454545456042e-2 3.3253355371900814e-2 6.0172553719008266e-2 3.515305785123989e-2 9.83027710710737e-4 3.507299122388432e-3 5.946503933885027e-4 1.3095898869752052e-3 5.8164262194012025e-3 1.7949836549962473e-3 3.618825620246443e-2 7.626549822430326e-2 4.23672474323769e-2
user_002 Sitting 1.446034568082e12 -0.689167 -0.191003 9.4262 -0.7553561818181819 -0.156166 9.429683636363636 0.47495115388899994 3.6482146009e-2 88.85324643999999 9.45328936084673 9.461340420717299 6.61891818181819e-2 3.483700000000001e-2 3.4836363636365775e-3 2.0585446280991692e-2 3.64201570247934e-2 2.406876033057886e-2 4.381007789760341e-3 1.2136165690000004e-3 1.2135722314051077e-5 7.68973975536438e-4 1.973744353509392e-3 7.92131692862547e-4 2.77303800106749e-2 4.442684271371748e-2 2.8144834212738702e-2
user_002 Sitting 1.446034569054e12 -0.765807 -0.191003 9.38788 -0.7553561818181819 -0.14223136363636366 9.433167272727273 0.586460361249 3.6482146009e-2 88.13229089439999 9.420999596733777 9.464589357092626 1.0450818181818144e-2 4.877163636363635e-2 4.528727272727373e-2 2.7235760330578507e-2 3.4520107438016534e-2 4.1803636363636994e-2 1.0921960066942071e-4 2.3786725135867756e-3 2.050937071074471e-3 1.3459775338459804e-3 2.280452508866266e-3 2.2395923906837365e-3 3.6687566474842406e-2 4.775408368785088e-2 4.732433190953399e-2
user_002 Sitting 1.446034569312e12 -0.727487 -0.152682 9.4262 -0.7379379090909091 -0.1352639090909091 9.429683636363634 0.529237335169 2.3311793124000002e-2 88.85324643999999 9.455463794457307 9.459572879689434 1.0450909090909066e-2 1.741809090909091e-2 3.483636363634801e-3 2.153520661157021e-2 2.7869371900826445e-2 2.4702148760331447e-2 1.0922150082644576e-4 3.0338989091735545e-4 1.21357223140387e-5 6.707759668279468e-4 1.524711514772352e-3 1.0194006743802228e-3 2.589934298062302e-2 3.904755452998756e-2 3.192805466012959e-2
user_002 Sitting 1.446034569443e12 -0.727487 -0.152682 9.4262 -0.7449052727272728 -0.14571481818181817 9.429683636363636 0.529237335169 2.3311793124000002e-2 88.85324643999999 9.455463794457307 9.460267960270265 1.741827272727281e-2 6.9671818181818446e-3 3.4836363636365775e-3 2.3752140495867744e-2 1.741833884297521e-2 1.8051570247934832e-2 3.033962248016558e-4 4.8541622487603674e-5 1.2135722314051077e-5 8.925379239271204e-4 6.343758824335087e-4 5.858244135237135e-4 2.9875373201470143e-2 2.5186819617282145e-2 2.4203809896867757e-2
user_002 Sitting 1.446034569896e12 -0.765807 -0.152682 9.4262 -0.7518726363636362 -0.14919836363636363 9.436650909090908 0.586460361249 2.3311793124000002e-2 88.85324643999999 9.458489234247349 9.467781289695454 1.39343636363638e-2 3.483636363636383e-3 1.0450909090907956e-2 2.4068917355371876e-2 1.2034322314049604e-2 1.74181818181824e-2 1.9416648995041779e-4 1.2135722314049723e-5 1.0922150082642256e-4 8.362799605987959e-4 1.8313804574830977e-4 4.909451299774928e-4 2.8918505504240636e-2 1.353285061427598e-2 2.2157281646842258e-2
user_002 Sitting 1.446034570996e12 -0.765807 -0.114362 9.4262 -0.7623234545454545 -0.15964954545454543 9.429683636363636 0.586460361249 1.3078667044000002e-2 88.85324643999999 9.457948269487044 9.461895649839656 3.4835454545455447e-3 4.528754545454543e-2 3.4836363636365775e-3 1.266781818181819e-2 3.166985123966942e-2 2.6919008264463008e-2 1.2135088933884925e-5 2.050961773297518e-3 1.2135722314051077e-5 3.552524841622832e-4 1.5004470374380165e-3 1.1639260946656388e-3 1.884814272447774e-2 3.873560426065426e-2 3.411636109941444e-2
user_002 Sitting 1.446034571061e12 -0.765807 -0.152682 9.46452 -0.7623234545454545 -0.15268218181818183 9.433167272727273 0.586460361249 2.3311793124000002e-2 89.5771388304 9.496678945019307 9.465226755939694 3.4835454545455447e-3 1.8181818181584042e-7 3.135272727272742e-2 1.266782644628101e-2 2.5335917355371895e-2 2.691900826446317e-2 1.2135088933884925e-5 3.3057851238818004e-14 9.829935074380258e-4 3.5525254174079574e-4 1.0591388312321563e-3 1.1639260946656493e-3 1.8848144251909674e-2 3.25444132107518e-2 3.411636109941459e-2
user_002 Sitting 1.446034571579e12 -0.727487 -0.229323 9.4262 -0.7623234545454545 -0.1666169090909091 9.440134545454544 0.529237335169 5.2589038329e-2 88.85324643999999 9.45701183321127 9.472428728452309 3.4836454545454476e-2 6.27060909090909e-2 1.3934545454544534e-2 1.1401074380165273e-2 2.7236082644628092e-2 2.8185785123967057e-2 1.213578565297516e-3 3.932053837099173e-3 1.941715570247677e-4 3.5966524376709197e-4 1.3084813420150261e-3 1.1551001148009435e-3 1.8964842307994337e-2 3.617293659650853e-2 3.398676381771209e-2
user_002 Sitting 1.44603457242e12 -0.727487 -0.152682 9.46452 -0.7449052727272726 -0.15964963636363638 9.429683636363633 0.529237335169 2.3311793124000002e-2 89.5771388304 9.493665675527708 9.460525402885743 1.7418272727272588e-2 6.96763636363637e-3 3.483636363636755e-2 3.0719429752066107e-2 2.4069231404958678e-2 2.8819173553719324e-2 3.0339622480164805e-4 4.854795649586786e-5 1.2135722314052313e-3 1.4662266727175069e-3 7.303726936025546e-4 1.2874898127723882e-3 3.829133939571071e-2 2.7025408296685448e-2 3.588160828018148e-2
user_002 Sitting 1.446034572485e12 -0.727487 -7.6042e-2 9.46452 -0.7449052727272728 -0.14919863636363637 9.436650909090908 0.529237335169 5.782385764e-3 89.5771388304 9.492742414673064 9.46732361206275 1.741827272727281e-2 7.315663636363637e-2 2.786909090909262e-2 2.976934710743803e-2 2.9136322314049588e-2 2.6285619834711227e-2 3.033962248016558e-4 5.351893444041323e-3 7.766862280992689e-4 1.4231998481344878e-3 1.1893258346273481e-3 1.075666296018078e-3 3.772532104746741e-2 3.448660369806438e-2 3.279735196655483e-2
user_002 Sitting 1.446034573133e12 -0.765807 -0.152682 9.4262 -0.7518726363636364 -0.1491987272727273 9.436650909090908 0.586460361249 2.3311793124000002e-2 88.85324643999999 9.458489234247349 9.467899249887088 1.3934363636363578e-2 3.4832727272727237e-3 1.0450909090907956e-2 2.6919157024793385e-2 3.705350413223141e-2 1.551801652892512e-2 1.941664899504116e-4 1.2133188892561959e-5 1.0922150082642256e-4 1.007281305137491e-3 1.8832758142344102e-3 2.7470862329075655e-4 3.173769533437315e-2 4.339672584693931e-2 1.6574336285075084e-2
user_002 Sitting 1.446034573262e12 -0.765807 -0.114362 9.46452 -0.7518725454545454 -0.1491987272727273 9.440134545454544 0.586460361249 1.3078667044000002e-2 89.5771388304 9.496140155805042 9.471356114557238 1.3934454545454611e-2 3.483672727272728e-2 2.4385454545456042e-2 2.3435413223140508e-2 3.7686917355371906e-2 1.678479338842949e-2 1.9416902347934065e-4 1.2135975670743808e-3 5.946503933885027e-4 8.208248797753583e-4 1.9229953147746053e-3 3.1883852261458203e-4 2.8650041531826063e-2 4.385197047767187e-2 1.785605002834003e-2
user_002 Sitting 1.446034573392e12 -0.765807 -0.152682 9.38788 -0.7518725454545454 -0.145715 9.433167272727273 0.586460361249 2.3311793124000002e-2 88.13229089439999 9.420300581657306 9.46434959120047 1.3934454545454611e-2 6.967000000000001e-3 4.528727272727373e-2 2.3435413223140508e-2 3.483659504132232e-2 1.931834710743775e-2 1.9416902347934065e-4 4.8539089000000014e-5 2.050937071074471e-3 8.119984968407226e-4 1.7674321221953415e-3 4.732931702479435e-4 2.849558732226312e-2 4.204083874276703e-2 2.1755302118057186e-2
user_002 Sitting 1.446034573455e12 -0.689167 -0.152682 9.38788 -0.7518725454545454 -0.14223127272727273 9.4262 0.47495115388899994 2.3311793124000002e-2 88.13229089439999 9.414380162358698 9.457338697442692 6.270554545454543e-2 1.0450727272727278e-2 3.8320000000000576e-2 2.3435404958677686e-2 3.1669578512396705e-2 2.0585123966941798e-2 3.931985430752063e-3 1.0921770052892573e-4 1.4684224000000442e-3 8.119974603846732e-4 1.590906939531931e-3 5.52726989030811e-4 2.849556913600206e-2 3.988617479192422e-2 2.351014651232125e-2
user_002 Sitting 1.446034573521e12 -0.727487 -0.114362 9.46452 -0.7483889090909092 -0.13178027272727272 9.429683636363636 0.529237335169 1.3078667044000002e-2 89.5771388304 9.493126715293176 9.460346873567627 2.0901909090909165e-2 1.7418272727272713e-2 3.4836363636364e-2 2.4068818181818195e-2 2.628573553719008e-2 2.2485289256198113e-2 4.368898036446312e-4 3.033962248016524e-4 1.213572231404984e-3 8.340632161750568e-4 1.0845090320075132e-3 6.453997776108307e-4 2.8880152634206364e-2 3.293188473208773e-2 2.540471959323367e-2
user_002 Sitting 1.44603457378e12 -0.804128 -0.114362 9.4262 -0.748388909090909 -0.13874763636363638 9.426199999999996 0.646621840384 1.3078667044000002e-2 88.85324643999999 9.461128206901542 9.4570153963809 5.5739090909090905e-2 2.4385636363636373e-2 3.552713678800501e-15 2.5018917355371924e-2 3.325310743801653e-2 2.121851239669487e-2 3.1068462553719e-3 5.946592608595046e-4 1.2621774483536189e-29 9.057764906649138e-4 1.7641233586235911e-3 7.160076165289551e-4 3.0096120857428018e-2 4.20014685293692e-2 2.6758318641666466e-2
user_002 Sitting 1.446034574557e12 -0.727487 -0.191003 9.38788 -0.7449053636363636 -0.16661700000000002 9.436650909090908 0.529237335169 3.6482146009e-2 88.13229089439999 9.417962113725983 9.467609675926727 1.741836363636362e-2 2.438599999999999e-2 4.877090909090853e-2 3.198629752066117e-2 2.6919380165289254e-2 2.8185785123967865e-2 3.033993917685945e-4 5.946769959999995e-4 2.3786015735536644e-3 1.3537044120946666e-3 1.1264441829564242e-3 1.1948170241924013e-3 3.679272227077886e-2 3.356254136617822e-2 3.456612538588034e-2
user_002 Sitting 1.446034574622e12 -0.765807 -0.114362 9.46452 -0.7483889999999999 -0.16661700000000002 9.44361818181818 0.586460361249 1.3078667044000002e-2 89.5771388304 9.496140155805042 9.474829731901304 1.7418000000000156e-2 5.225500000000001e-2 2.0901818181819465e-2 3.1986272727272766e-2 2.913626446280992e-2 2.7552396694216084e-2 3.033867240000054e-4 2.730585025000001e-3 4.368860033058388e-4 1.3537035483854264e-3 1.304070425015778e-3 1.1639260946657436e-3 3.679271053327583e-2 3.611191527758917e-2 3.411636109941597e-2
user_002 Sitting 1.446034576823e12 -0.727487 -0.191003 9.4262 -0.7518726363636364 -0.1666169090909091 9.422716363636361 0.529237335169 3.6482146009e-2 88.85324643999999 9.456160210211014 9.454272305984539 2.4385636363636443e-2 2.4386090909090913e-2 3.483636363638354e-3 2.7869264462809906e-2 2.7869404958677693e-2 2.7552396694216084e-2 5.94659260859508e-4 5.946814298264465e-4 1.2135722314063454e-5 1.0657548036927113e-3 1.4430797806025554e-3 1.042568871525224e-3 3.2645900258573224e-2 3.7987889920375356e-2 3.228883509086731e-2
user_002 Sitting 1.446034577018e12 -0.727487 -0.114362 9.4262 -0.7553562727272727 -0.16661700000000002 9.408781818181815 0.529237335169 1.3078667044000002e-2 88.85324643999999 9.454922656596032 9.440658106002388 2.7869272727272687e-2 5.225500000000001e-2 1.7418181818184664e-2 2.4385636363636352e-2 3.51535041322314e-2 2.4385454545455074e-2 7.766963623471052e-4 2.730585025000001e-3 3.033930578513388e-4 7.822206036093154e-4 1.9318294136048094e-3 8.10886900075132e-4 2.7968207014560577e-2 4.395258142140015e-2 2.847607592480277e-2
user_002 Sitting 1.446034577212e12 -0.765807 -0.191003 9.38788 -0.75884 -0.16661700000000002 9.408781818181815 0.586460361249 3.6482146009e-2 88.13229089439999 9.420999596733777 9.440918817313054 6.9670000000000565e-3 2.438599999999999e-2 2.0901818181815912e-2 2.6919239669421488e-2 2.9136363636363637e-2 2.3752066115702967e-2 4.853908900000079e-5 5.946769959999995e-4 4.3688600330569024e-4 9.455071044012013e-4 1.3239312405514654e-3 8.175063849737142e-4 3.074909924536329e-2 3.6385865944779515e-2 2.8592068567589057e-2
user_002 Sitting 1.446034577795e12 -0.727487 -0.152682 9.4262 -0.7483890000000002 -0.16313327272727274 9.426199999999998 0.529237335169 2.3311793124000002e-2 88.85324643999999 9.455463794457307 9.45736017391277 2.0902000000000198e-2 1.0451272727272726e-2 1.7763568394002505e-15 2.6919165289256218e-2 1.9002099173553715e-2 2.2801983471075295e-2 4.3689360400000826e-4 1.0922910161983468e-4 3.1554436208840472e-30 1.0315537862246422e-3 4.5014236270323057e-4 1.081182533433542e-3 3.211781104347932e-2 2.121655869134367e-2 3.28813402012987e-2
user_002 Sitting 1.446034578183e12 -0.765807 -0.191003 9.46452 -0.7414216363636363 -0.15268236363636364 9.429683636363636 0.586460361249 3.6482146009e-2 89.5771388304 9.497372338581762 9.460134033566653 2.4385363636363677e-2 3.832063636363636e-2 3.4836363636364e-2 2.7235842975206627e-2 2.8502917355371894e-2 1.9635041322314608e-2 5.94645959677688e-4 1.4684711713140496e-3 1.213572231404984e-3 9.598359052314046e-4 1.2091844044785874e-3 6.509160150263041e-4 3.09812185885482e-2 3.477332892431479e-2 2.5513055775941543e-2
user_002 Sitting 1.446034578766e12 -0.765807 -0.191003 9.4262 -0.7623234545454545 -0.1666170909090909 9.4262 0.586460361249 3.6482146009e-2 88.85324643999999 9.459185427258417 9.45851288519757 3.4835454545455447e-3 2.4385909090909097e-2 0.0 1.7101537190082703e-2 2.501930578512396e-2 2.7552396694215112e-2 1.2135088933884925e-5 5.94672562190083e-4 0.0 4.6778447599248973e-4 8.528350068084148e-4 1.2411534184823567e-3 2.162832577876729e-2 2.9203338966775953e-2 3.523000735853395e-2
user_002 Sitting 1.446034581033e12 -0.727487 -0.191003 9.4262 -0.7623234545454545 -0.17010063636363637 9.478454545454545 0.529237335169 3.6482146009e-2 88.85324643999999 9.456160210211014 9.510648892733103 3.4836454545454476e-2 2.0902363636363636e-2 5.225454545454511e-2 1.741826446280995e-2 2.4068983471074386e-2 2.4702148760330965e-2 1.213578565297516e-3 4.369088055867768e-4 2.730537520661121e-3 4.4682294655447097e-4 1.0150141718414728e-3 1.1650293421487457e-3 2.1138186926850443e-2 3.185928705795961e-2 3.413252616125483e-2
user_002 Sitting 1.446309384445e12 -0.535886 3.2195 9.04299 -0.7309707272727273 0.7809393636363636 9.339108181818183 0.287173804996 10.36518025 81.7756681401 9.613949354718693 9.520764338343959 0.1950847272727273 2.4385606363636363 0.2961181818181835 7.220688429752066e-2 0.7740065371900826 9.595917355371855e-2 3.805805081507439e-2 5.946577977222223 8.768597760330678e-2 8.956297980089407e-3 2.098633624418471 2.3968541014275056e-2 9.4637719647556e-2 1.4486661535421028 0.15481776711435627
user_002 Sitting 1.44630938464e12 -0.574206 3.2195 9.04299 -0.6891668181818181 1.6901753636363637 9.234597272727274 0.329712530436 10.36518025 81.7756681401 9.616161444180104 9.565677478020099 0.11496081818181814 1.5293246363636364 0.19160727272727485 9.627590082644626e-2 1.260769123966942 0.14789826446281007 1.3215989717033047e-2 2.3388338433887688 3.6713346961984285e-2 1.2208727644145751e-2 3.044968591590844 3.636520440157808e-2 0.11049311129724672 1.744983837057193 0.19069662923496597
user_002 Sitting 1.446309385353e12 -0.574206 3.2195 9.15796 -0.5602713636363635 3.2334381818181814 9.098737272727274 0.329712530436 10.36518025 83.86823136159998 9.724357261127132 9.672577064669866 1.3934636363636455e-2 1.3938181818181405e-2 5.9222727272725706e-2 4.877124793388429e-2 0.2900964710743801 5.162198347107379e-2 1.941740905867794e-4 1.942729123966827e-4 3.507331425619649e-3 3.8702471030300487e-3 0.2611054662973247 3.4476748824943267e-3 6.221131008932418e-2 0.5109848004562608 5.871690457180391e-2
user_002 Sitting 1.446309386194e12 -0.574206 3.25783 9.15796 -0.5707223636363637 3.216019090909091 9.154476363636364 0.329712530436 10.613456308899998 83.86823136159998 9.73711457265118 9.719867683131518 3.4836363636362444e-3 4.181090909090868e-2 3.483636363634801e-3 2.2485289256198335e-2 3.674115702479346e-2 2.9135950413221225e-2 1.2135722314048756e-5 1.74815211900823e-3 1.21357223140387e-5 7.138006609226137e-4 1.8670350664162334e-3 1.1142885951163555e-3 2.6717048132655182e-2 4.320920117771484e-2 3.338096156668282e-2
user_002 Sitting 1.446309386519e12 -0.612526 3.2195 9.15796 -0.5776896363636363 3.2125354545454545 9.161443636363638 0.375188100676 10.36518025 83.86823136159998 9.726695210207627 9.725675974401412 3.4836363636363665e-2 6.964545454545501e-3 3.483636363638354e-3 2.2801975206611588e-2 3.674016528925614e-2 2.6919008264462363e-2 1.2135722314049607e-3 4.85048933884304e-5 1.2135722314063454e-5 7.347622479376416e-4 1.7588897474079622e-3 1.2764573379413628e-3 2.7106498260336793e-2 4.1939119535440446e-2 3.572754312769579e-2
user_002 Sitting 1.446309386648e12 -0.574206 3.18118 9.15796 -0.5776896363636363 3.1951163636363633 9.164927272727274 0.329712530436 10.1199061924 83.86823136159998 9.711737747923179 9.72320484422079 3.4836363636363554e-3 1.3936363636363414e-2 6.967272727274931e-3 2.3752057851239683e-2 3.610619834710726e-2 2.6285619834710904e-2 1.213572231404953e-5 1.9422223140495246e-4 4.854288925622906e-5 8.042663787250196e-4 1.7125032538692574e-3 1.2764573379413818e-3 2.8359590595158805e-2 4.1382402707784594e-2 3.5727543127696056e-2
user_002 Sitting 1.44630938775e12 -0.612526 3.2195 9.15796 -0.6090425454545454 3.2299545454545453 9.185829090909092 0.375188100676 10.36518025 83.86823136159998 9.726695210207627 9.756244658044482 3.483454545454623e-3 1.0454545454545272e-2 2.786909090909262e-2 2.2168702479338845e-2 2.217214876033062e-2 2.913586776859457e-2 1.2134455570248473e-5 1.0929752066115321e-4 7.766862280992689e-4 7.612562527084889e-4 8.453236761081932e-4 1.0061617045830013e-3 2.7590872634052167e-2 2.9074450572765656e-2 3.1720052089853214e-2
user_002 Sitting 1.446309387944e12 -0.612526 3.2195 9.19628 -0.6055588181818181 3.2299545454545453 9.178861818181819 0.375188100676 10.36518025 84.57156583839999 9.762783116974175 9.749457085970963 6.9671818181819e-3 1.0454545454545272e-2 1.741818181818111e-2 1.6784818181818206e-2 2.058909090909091e-2 2.6602314049586958e-2 4.8541622487604446e-5 1.0929752066115321e-4 3.0339305785121503e-4 4.1151804822614626e-4 7.051859090909096e-4 8.693590166792054e-4 2.0285907626383055e-2 2.6555336734654857e-2 2.948489472050401e-2
user_002 Sitting 1.446309388852e12 -0.650847 3.2195 9.19628 -0.605558909090909 3.1985981818181823 9.178861818181819 0.42360181740899994 10.36518025 84.57156583839999 9.765262306042219 9.739133143241268 4.528809090909092e-2 2.090181818181769e-2 1.741818181818111e-2 2.1218752066115697e-2 2.09031404958675e-2 3.198611570247953e-2 2.0510111781900835e-3 4.368860033057645e-4 3.0339305785121503e-4 9.432911661134501e-4 5.847724156273373e-4 1.7001043714500528e-3 3.0713045536277416e-2 2.4182068059356243e-2 4.123232192649418e-2
user_002 Sitting 1.446309389111e12 -0.612526 3.06622 9.19628 -0.6020752727272728 3.1776963636363633 9.178861818181817 0.375188100676 9.4017050884 84.57156583839999 9.713313493729933 9.73214500267062 1.0450727272727223e-2 0.11147636363636337 1.7418181818182887e-2 2.5969099173553722e-2 3.388677685950394e-2 2.7869090909091166e-2 1.0921770052892458e-4 1.2426979649586719e-2 3.0339305785127694e-4 1.1209155655725032e-3 1.863416669120946e-3 1.3945048186326265e-3 3.3480077144064396e-2 4.316731019094132e-2 3.734306921816452e-2
user_002 Sitting 1.446309389241e12 -0.574206 3.18118 9.15796 -0.5985916363636363 3.1811800000000003 9.16841090909091 0.329712530436 10.1199061924 83.86823136159998 9.711737747923179 9.723228537958677 2.438563636363633e-2 4.440892098500626e-16 1.0450909090911509e-2 2.8819330578512402e-2 3.1036198347107306e-2 2.6285619834711064e-2 5.946592608595026e-4 1.9721522630525295e-31 1.0922150082649681e-4 1.183801075178814e-3 1.7762370850488212e-3 1.298522287603321e-3 3.4406410379154845e-2 4.214542780716339e-2 3.603501474404196e-2
user_002 Sitting 1.44630938937e12 -0.650847 3.2195 9.15796 -0.605559 3.1881472727272726 9.164927272727274 0.42360181740899994 10.36518025 83.86823136159998 9.72918359519487 9.722676512480279 4.5287999999999995e-2 3.135272727272742e-2 6.967272727274931e-3 3.0402834710743808e-2 3.23028099173553e-2 2.4385454545455234e-2 2.0510029439999994e-3 9.829935074380258e-4 4.854288925622906e-5 1.2533105034477844e-3 1.8512492766340983e-3 1.249979398347126e-3 3.5402125691090705e-2 4.3026146430212624e-2 3.535504770675788e-2
user_002 Sitting 1.446309389629e12 -0.612526 3.2195 9.19628 -0.6125262727272726 3.2020827272727272 9.178861818181819 0.375188100676 10.36518025 84.57156583839999 9.762783116974175 9.740828570831246 2.727272725433494e-7 1.741727272727278e-2 1.741818181818111e-2 2.565241322314048e-2 3.7053966942148756e-2 1.995173553719034e-2 7.438016518893438e-14 3.0336138925620023e-4 3.0339305785121503e-4 1.1727739554079622e-3 2.5872794606310967e-3 5.262490494365031e-4 3.424578741112492e-2 5.086530704351539e-2 2.294011877555352e-2
user_002 Sitting 1.446309390018e12 -0.574206 3.25783 9.15796 -0.5985916363636364 3.2195027272727272 9.171894545454547 0.329712530436 10.613456308899998 83.86823136159998 9.73711457265118 9.739064553708165 2.4385636363636443e-2 3.832727272727254e-2 1.3934545454548086e-2 2.6285809917355386e-2 3.388867768595041e-2 2.058512396694293e-2 5.94659260859508e-4 1.4689798347107296e-3 1.9417155702486672e-4 1.1484991710375648e-3 1.78206255048835e-3 5.615529688955879e-4 3.388951417529565e-2 4.2214482710183125e-2 2.3697108872087917e-2
user_002 Sitting 1.446309391119e12 -0.574206 3.10454 9.15796 -0.5567876363636364 3.1846645454545457 9.144025454545456 0.329712530436 9.638168611600001 83.86823136159998 9.686904175413112 9.698858991586967 1.741836363636362e-2 8.012454545454561e-2 1.3934545454542757e-2 3.293638842975204e-2 2.2486859504132376e-2 1.5201322314048905e-2 3.033993917685945e-4 6.419942784297547e-3 1.9417155702471822e-4 1.478373393021036e-3 1.071374233433505e-3 3.6848465935387405e-4 3.844962149385916e-2 3.273185349828978e-2 1.9195954244420204e-2
user_002 Sitting 1.446309392026e12 -0.535886 3.2195 9.11964 -0.5533039999999999 3.1846645454545457 9.144025454545456 0.287173804996 10.36518025 83.1678337296 9.686082168998775 9.698729922258241 1.7417999999999934e-2 3.4835454545454336e-2 2.4385454545456042e-2 3.515319008264461e-2 3.1037024793388455e-2 4.3070413223140716e-2 3.033867239999977e-4 1.2135088933884152e-3 5.946503933885027e-4 1.567734366244927e-3 1.279874187678435e-3 2.2837222900075043e-3 3.959462547170925e-2 3.577532931614236e-2 4.77883070427014e-2
user_002 Sitting 1.44630939248e12 -0.612526 3.18118 9.15796 -0.5672386363636364 3.163761818181818 9.14054181818182 0.375188100676 10.1199061924 83.86823136159998 9.714078734222612 9.689318632790359 4.528736363636365e-2 1.7418181818182e-2 1.7418181818179335e-2 3.103615702479341e-2 2.1218677685950326e-2 3.0402644628098455e-2 2.050945305132233e-3 3.03393057851246e-4 3.033930578511532e-4 1.2621281915815193e-3 5.747948180315491e-4 1.5489594662659756e-3 3.552644355380256e-2 2.397487889503405e-2 3.9356822359865075e-2
user_002 Sitting 1.446309392932e12 -0.497565 3.14286 9.15796 -0.5672386363636364 3.1672463636363632 9.133574545454547 0.24757092922499999 9.877568979600001 83.86823136159998 9.695017858179787 9.68388161177369 6.967363636363638e-2 2.438636363636304e-2 2.438545454545249e-2 2.3752181818181853e-2 2.8186694214875872e-2 3.325289256198293e-2 4.854415604132234e-3 5.946947314049296e-4 5.946503933883295e-4 9.89626005675433e-4 1.2677851063110273e-3 1.459596420135237e-3 3.1458321723757496e-2 3.5605970093665854e-2 3.8204664900182504e-2
user_002 Sitting 1.446309393711e12 -0.535886 3.14286 9.15796 -0.556787909090909 3.1742136363636364 9.137058181818183 0.287173804996 9.877568979600001 83.86823136159998 9.697060077476884 9.688865473566398 2.0901909090909054e-2 3.1353636363636195e-2 2.0901818181815912e-2 2.7552495867768565e-2 3.452090909090896e-2 2.8502479338842302e-2 4.368898036446266e-4 9.830505132231299e-4 4.3688600330569024e-4 1.119810475468068e-3 1.5546162080390586e-3 1.0547045938392187e-3 3.3463569377280546e-2 3.9428621685763485e-2 3.2476215817721414e-2
user_002 Sitting 1.446309393905e12 -0.574206 3.18118 9.11964 -0.5637551818181818 3.1776972727272725 9.13009090909091 0.329712530436 10.1199061924 83.1678337296 9.67561121854511 9.683824991940147 1.0450818181818144e-2 3.4827272727273595e-3 1.0450909090909732e-2 2.3752148760330553e-2 3.198719008264464e-2 2.2485289256198113e-2 1.0921960066942071e-4 1.2129389256198951e-5 1.092215008264597e-4 9.477042135897816e-4 1.4509074897821168e-3 6.983556567994071e-4 3.07848049139471e-2 3.8090779590107066e-2 2.6426419674246585e-2
user_002 Sitting 1.446309394877e12 -0.574206 3.18118 9.19628 -0.5811733636363636 3.1881472727272726 9.157960000000001 0.329712530436 10.1199061924 84.57156583839999 9.747881029292264 9.714549440349064 6.967363636363633e-3 6.967272727272711e-3 3.83199999999988e-2 2.628568595041322e-2 1.741818181818192e-2 3.2302809917355096e-2 4.854415604132226e-5 4.854288925619812e-5 1.468422399999908e-3 1.1705577291855754e-3 5.174230695717485e-4 1.2047462515401587e-3 3.4213414462540497e-2 2.2746935388569346e-2 3.470945478598243e-2
user_002 Sitting 1.446309396108e12 -0.574206 3.2195 9.15796 -0.5707222727272727 3.1951154545454545 9.130090909090912 0.329712530436 10.36518025 83.86823136159998 9.724357261127132 9.689957950948695 3.4837272727272772e-3 2.4384545454545492e-2 2.786909090908729e-2 2.47022479338843e-2 2.5653553719008248e-2 3.1352727272726776e-2 1.2136355710743833e-5 5.946060570247952e-4 7.766862280989719e-4 1.059130078879039e-3 8.617503026296023e-4 1.2985222876033054e-3 3.254427874264598e-2 2.935558384072104e-2 3.6035014744041735e-2
user_002 Sitting 1.44630939669e12 -0.574206 3.2195 9.11964 -0.5637550909090909 3.2020827272727272 9.130090909090912 0.329712530436 10.36518025 83.1678337296 9.688277788649332 9.69178806671138 1.0450909090909066e-2 1.741727272727278e-2 1.0450909090911509e-2 1.8368264462809904e-2 1.9952644628099234e-2 1.900165289256186e-2 1.0922150082644576e-4 3.0336138925620023e-4 1.0922150082649681e-4 6.023727802885044e-4 5.52808184748308e-4 5.758951861757681e-4 2.4543283812246974e-2 2.3511873271781388e-2 2.3997816279315253e-2
user_002 Sitting 1.446309397792e12 -0.650847 3.18118 9.11964 -0.5811733636363636 3.181180909090909 9.137058181818183 0.42360181740899994 10.1199061924 83.1678337296 9.680461855686897 9.692544633867705 6.967363636363633e-2 9.0909090921798e-7 1.7418181818182887e-2 2.0901900826446305e-2 2.2486611570248005e-2 2.8185785123966575e-2 4.854415604132226e-3 8.264462812227735e-13 3.0339305785127694e-4 9.620433214876044e-4 1.0404994267468033e-3 1.2786638329075584e-3 3.1016823201088863e-2 3.225677334679963e-2 3.575840926142491e-2
user_002 Sitting 1.446309397857e12 -0.574206 3.18118 9.11964 -0.5776897272727273 3.181180909090909 9.14054181818182 0.329712530436 10.1199061924 83.1678337296 9.67561121854511 9.695612912433567 3.4837272727272772e-3 9.0909090921798e-7 2.0901818181819465e-2 1.741827272727274e-2 2.1536528925619927e-2 2.6602314049586472e-2 1.2136355710743833e-5 8.264462812227735e-13 4.368860033058388e-4 8.042789889864763e-4 1.0305684719759548e-3 1.184887796844449e-3 2.835981292227571e-2 3.210246831594036e-2 3.4422199186636075e-2
user_002 Sitting 1.446309398115e12 -0.612526 3.10454 9.11964 -0.584657 3.1776972727272734 9.133574545454548 0.375188100676 9.638168611600001 83.1678337296 9.653040476548103 9.688387501624 2.7869000000000033e-2 7.315727272727335e-2 1.3934545454548086e-2 3.008621487603306e-2 2.407008264462823e-2 2.4385454545455234e-2 7.766811610000018e-4 5.351986552892653e-3 1.9417155702486672e-4 1.797227750449286e-3 1.3549278384673222e-3 8.550167993989645e-4 4.239372300764921e-2 3.680934444495476e-2 2.924067029667693e-2
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user_002 Walking 1.446034833993e12 -5.13432 -10.72909 -1.03525 -4.719765454545455 -8.781721818181817 1.6053699999999997 26.361241862399996 115.11337222809999 1.0717425625 11.939277894956629 10.229185323152004 0.4145545454545445 1.9473681818181827 2.6406199999999997 1.0422495041322313 0.9972793388429756 1.484041785123967 0.17185547115702401 3.792242835557855 6.972873984399999 1.6845566543133723 1.870692309292413 2.9552987618498023 1.297904716962448 1.3677325430406388 1.7190982408954418
user_002 Walking 1.446034834964e12 -5.63249 -7.62514 2.22198 -5.1621927272727275 -8.429870000000001 0.5324010909090909 31.724943600099998 58.1427600196 4.937195120399999 9.736780717470225 10.26159981256484 0.4702972727272723 0.8047300000000011 1.6895789090909088 1.654741322314049 0.785725123966942 1.952752859504132 0.22117952473471034 0.6475903729000017 2.8546768900448254 4.7563571161466545 1.769143344757024 4.538077592130551 2.1809074065963125 1.3300914798452863 2.13027641214246
user_002 Walking 1.446034835029e12 -5.47921 -9.69444 1.72382 -4.914852727272727 -8.691144545454547 1.016631090909091 30.021742224100002 93.9821669136 2.9715553923999996 11.268339031556515 10.262562882771157 0.564357272727273 1.0032954545454533 0.7071889090909089 1.403284132231404 0.7594395041322314 1.6715264958677687 0.3184991312801656 1.0066017691115678 0.5001161531411898 3.776995825254394 1.7087985870458295 3.2703463178698895 1.9434494655777377 1.3072102306231501 1.8084098865771248
user_003 Jogging 1.446047735631e12 -6.13065 1.30349 -1.41845 -4.1150026 -9.376380999999999 -0.4642744 37.5848694225 1.6990861801000001 2.0120004025 6.426192963574935 11.322521733313769 2.0156473999999998 10.679870999999999 0.9541756 1.508743876269841 5.754433644047619 0.7064118632539683 4.062834441126759 114.05964457664096 0.91045107563536 3.8693449254455743 46.70436439108835 0.6896023926483791 1.9670650536892709 6.834059144541284 0.8304230203025318
user_003 Jogging 1.446047736344e12 -8.08499 -2.91174 -0.996927 -4.580421818181818 -9.544642454545455 -0.9098348181818181 65.36706330009999 8.4782298276 0.993863443329 8.65096275399617 11.846538256703665 3.5045681818181817 6.632902454545455 8.70921818181819e-2 2.3809260991735535 7.526616578512397 1.4064498429752066 12.281998141012396 43.995394971515125 7.585048133851254e-3 7.642918822991907 62.51352266285617 3.284973150723658 2.764582938345657 7.906549352458136 1.8124494891509826
user_003 Jogging 1.446047736408e12 -6.82042 3.29615 -1.49509 -4.886984545454545 -9.18234090909091 -0.9690569999999998 46.51812897640001 10.864604822499999 2.2352941081 7.721271132851119 12.219725568362337 1.9334354545454557 12.478490909090908 0.5260330000000002 2.501587785123967 7.943073272727275 1.422918 3.738172656893393 155.71273536826445 0.2767107170890002 7.949350319215065 70.99922684871673 3.299315536658378 2.8194592246058576 8.426103894963362 1.8164018103543
user_003 Jogging 1.44604773699e12 -8.27659 -19.08292 1.30229 -4.524682272727272 -8.534378181818182 -0.2827757272727272 68.5019420281 364.15783572640004 1.6959592440999998 20.841202868323126 11.605676349077582 3.7519077272727284 10.54854181818182 1.585065727272727 2.701741016528926 7.784408900826446 1.9350175619834713 14.07681159396881 111.27173448993061 2.512433359774619 10.161299677030145 73.07180543440168 6.465091993559316 3.1876793560567136 8.548204807700953 2.5426545171452837
user_003 Jogging 1.446047737443e12 -4.21464 -13.52647 -1.22685 -5.256252636363637 -9.485420000000001 0.15964890909090904 17.7631903296 182.9653906609 1.5051609225 14.22089103794133 12.227766577150401 1.041612636363637 4.0410499999999985 1.386498909090909 2.7387941818181822 7.314746694214876 2.2672321322314053 1.0849568842324064 16.33008510249999 1.9223792249102807 10.071191419435943 64.77100283891653 6.9486060094820425 3.1735140490371148 8.048043416813588 2.6360208666628653
user_003 Jogging 1.446047737832e12 -4.25296 -10.65245 -1.49509 -4.273859 -9.380910000000002 9.694363636363644e-2 18.0876687616 113.4746910025 2.2352941081 11.5670935792964 11.598471252113294 2.0899e-2 1.2715399999999981 1.5920336363636365 2.398979024793389 7.37903520661157 2.1345355950413225 4.3676820100000004e-4 1.6168139715999952 2.5345710993132236 10.035068711816916 65.6950501683973 6.309138226020736 3.1678176576022987 8.105248310101135 2.5117997981568387
user_003 Jogging 1.446047739644e12 -4.75112 -18.58475 -1.61005 -3.939425818181818 -7.938673545454545 -0.23400472727272728 22.573141254400003 345.3929325625 2.5922610025 19.2498918131869 10.502220457474333 0.8116941818181824 10.646076454545454 1.3760452727272727 2.0211578677685953 6.429262074380166 1.5872846033057852 0.6588474447974886 113.33894387602712 1.8935005925950743 6.706053344980842 61.9598330580618 3.6706176146924605 2.5896048627118464 7.871456857409675 1.915885595408155
user_003 Jogging 1.446047740033e12 -3.71647 -18.85299 0.497565 -4.106641636363637 -8.123307272727272 -0.9168033636363636 13.812149260900002 355.4352319400999 0.24757092922499999 19.22225148441839 10.77016093587484 0.39017163636363694 10.729682727272726 1.4143683636363635 1.899864553719008 6.932174727272727 1.5011428016528925 0.15223390582267815 115.12609142793468 2.0004378680554047 5.325225943114908 67.17508940760895 3.137830390156627 2.3076451077050186 8.19604108137636 1.771392218046762
user_003 Jogging 1.446047740163e12 -7.28026 -6.20729 -1.34181 -4.381851636363636 -9.060413636363636 -0.8610647272727273 53.0021856676 38.530449144100004 1.8004540760999999 9.660905179526399 11.655872111868048 2.8984083636363644 2.8531236363636356 0.48074527272727263 2.1592396776859504 7.111743074380166 1.624021338842975 8.400771042397228 8.140314484376855 0.23111601724961975 6.355301068903384 68.16794305933205 3.3226541962235387 2.5209722467538955 8.256388015308634 1.8228149100288649
user_003 Jogging 1.446047741586e12 -5.70913 -13.79471 2.18366 -4.754604272727273 -8.084986727272726 -0.526633 32.5941653569 190.2940239841 4.768370995600001 15.088292161030022 11.192019763213258 0.9545257272727268 5.709723272727274 2.710293 2.1820410909090913 7.525347834710744 1.5670159504132235 0.911119364025528 32.60093985112345 7.345688145849 6.725637535457142 70.61233436629162 3.209251785542351 2.593383414664546 8.403114563439654 1.7914384682545899
user_003 Jogging 1.446047741846e12 -4.78944 -17.01362 1.26397 -4.639642454545454 -8.078019545454545 -0.5161817272727273 22.938735513599998 289.4632655044 1.5976201609 17.720034457610403 10.869660757908436 0.14979754545454593 8.935600454545455 1.7801517272727274 2.1741234710743806 7.074687818181817 1.5527638429752066 2.2439304624206756e-2 79.84495548327294 3.168940172112075 6.7387556156140995 62.02374722341651 3.395613878513215 2.595911326608461 7.875515679840686 1.8427191534558962
user_003 Jogging 1.446047741911e12 -9.92436 -19.08292 -5.13552 -4.942721545454545 -8.123307727272728 -0.7461035454545454 98.4929214096 364.15783572640004 26.373565670399998 22.113894338320424 11.071813299428175 4.981638454545455 10.959612272727274 4.389416454545454 2.4198804876033058 7.12631 1.7234634462809917 24.816721691806027 120.11310116851428 19.266976811434386 8.522939200738632 63.12595207200314 4.573635273326528 2.9194073372413505 7.945184206297745 2.1386059181921593
user_003 Jogging 1.446047742105e12 -1.37893 1.03525 -3.71767 -5.291088181818181 -8.461222363636365 -1.038731181818182 1.9014479449 1.0717425625 13.8210702289 4.09808012809657 11.394511324828654 3.912158181818181 9.496472363636364 2.678938818181818 2.4556680578512395 7.01831676859504 1.8542590495867766 15.304981639566938 90.18298735330924 7.1767131915613955 7.66219816374371 59.86598085424357 4.899174334712138 2.7680675865563162 7.737310957577159 2.213407855482613
user_003 Jogging 1.446047743076e12 -8.27659 -15.05928 -0.728685 -4.587387272727272 -9.088283272727274 0.2606753636363636 68.5019420281 226.78191411839998 0.530981829225 17.19926853025224 11.999853736250367 3.689202727272728 5.970996727272725 0.9893603636363637 3.0143207438016533 7.571903148760328 1.6436561157024796 13.610216762916535 35.65280191710159 0.9788339291346778 13.052084914282272 67.81846212605052 3.7920228341500035 3.612766933291196 8.235196544469993 1.9473116941440072
user_003 Jogging 1.44604774327e12 -2.29862 -1.18733 1.30229 -4.639641818181818 -8.698112363636362 -2.150190909090909e-2 5.2836539044 1.4097525289 1.6959592440999998 2.8964401732816785 11.391576321113966 2.3410218181818183 7.510782363636362 1.323791909090909 2.8230356198347106 7.835078702479337 1.5758826859504131 5.480383153203307 56.41185171391101 1.7524250185745534 12.127020067467846 69.08815603044107 2.9333404785628705 3.4823871219994835 8.311928538578822 1.7126997631116991
user_003 Jogging 1.446047743659e12 -6.28393 -8.54483 -3.90927 -4.674479090909092 -8.569216363636365 -0.5161813636363636 39.4877762449 73.01411972889998 15.282391932899998 11.304171261384 11.09202422756434 1.6094509090909082 2.438636363636526e-2 3.393088636363636 2.6973064462809915 7.171282801652893 1.653155925619835 2.590332228773551 5.946947314050379e-4 11.513050494220039 11.415731180268821 63.780162821143975 3.500501430448313 3.3787173868598157 7.9862483570913305 1.8709627015117947
user_003 Jogging 1.446047744112e12 -6.32225 -19.58108 -2.2615 -4.158896818181819 -8.503027454545455 -0.5684371818181818 39.970845062500004 383.4186939664 5.114382249999999 20.700336260044182 11.354514333727545 2.1633531818181817 11.078052545454545 1.6930628181818181 2.7714127272727276 6.852686305785125 1.8821283719008262 4.680096989282851 122.72324819985192 2.86646170630976 12.191477094626935 62.46514659401501 5.505646912405573 3.4916295758036724 7.903489520080039 2.346411496819254
user_003 Jogging 1.446047745795e12 -1.11069 0.958607 -2.68302 -3.9951649999999996 -9.670054818181818 -9.465881818181819e-2 1.2336322760999998 0.918927380449 7.1985963204 3.0579659868855638 11.578644378470724 2.8844749999999997 10.628661818181818 2.5883611818181818 2.4385655123966945 6.973345719008264 1.5590983388429753 8.320196025624998 112.96845204527604 6.699613607543214 8.35464690663406 56.06450086026842 3.6200956105588475 2.8904406076987743 7.487623178303541 1.9026548847751785
user_003 Jogging 1.446047746378e12 -8.23827 -17.32018 -1.26517 -4.576936636363637 -9.276400272727273 -0.4743760909090911 67.8690925929 299.98863523240004 1.6006551288999997 19.221300240987862 11.717208326426968 3.6613333636363627 8.043779727272728 0.7907939090909089 2.3983437355371895 6.6256129008264475 2.0360425371900828 13.405361999676762 64.70239230088373 0.6253550066552807 7.549598737169998 57.38207630334404 5.904934442457743 2.747653314588651 7.575095794994545 2.4300070869151273
user_003 Jogging 1.446047746443e12 -1.53221 -8.12331 1.72382 -4.806858181818182 -9.607348454545455 -0.282774090909091 2.3476674841 65.9881653561 2.9715553923999996 8.4443702093525 12.065932595475562 3.274648181818182 1.4840384545454555 2.0065940909090907 2.191540991735537 6.272179206611571 2.185207082644628 10.723320714685125 2.202370134569664 4.02641984567128 5.724745155916998 54.95898877838006 6.258809155594935 2.3926439676468787 7.413432995473828 2.5017612107463285
user_003 Jogging 1.446047747867e12 -8.54483 -16.05561 -3.25783 -4.4793936363636355 -8.879263090909092 -0.8645490000000001 73.01411972889998 257.7826124721 10.613456308899998 18.477288451228443 11.952809629959383 4.065436363636364 7.17634690909091 2.3932809999999995 2.48765438016529 7.61592423140496 1.7449996942148758 16.52777282677686 51.499954959618655 5.727793944960998 9.127936903276487 72.93491111289995 4.231126364306643 3.0212475739794127 8.540193856868822 2.056970190427329
user_003 Jogging 1.446047749615e12 -5.24928 -13.29655 1.22565 -4.2076685454545455 -8.370647363636364 -0.29670981818181813 27.5549405184 176.7982419025 1.5022179224999999 14.347661842383935 11.022203270846463 1.0416114545454542 4.925902636363636 1.522359818181818 1.9812563471074378 7.141195123966941 1.5100093719008265 1.084954422240297 24.264516782934223 2.317579416014578 6.159065103561868 61.365181331507394 3.5171502368635177 2.4817463817968726 7.833593130327065 1.8754066857253968
user_003 Jogging 1.446047750068e12 -8.12331 -17.09026 -4.40743 -4.510747636363636 -8.475158181818182 -0.7251994545454544 65.9881653561 292.0769868676 19.425439204899998 19.429117103682298 11.165998374535848 3.612562363636364 8.615101818181818 3.682230545454545 2.161772330578512 7.3682685950413225 1.7811014214876033 13.050606831161954 74.21997933763967 13.558821789878477 6.444378643861114 63.33591248530026 3.728135679427015 2.5385780751950717 7.958386299074723 1.9308380769570024
user_003 Jogging 1.446047750974e12 3.8919e-2 -0.842448 -1.57173 -3.8732365454545454 -9.973134363636364 -0.40122081818181826 1.514688561e-3 0.7097186327039999 2.4703351929000004 1.7836951853287601 11.766917856584998 3.9121555454545454 9.130686363636364 1.1705091818181819 2.1823563719008265 6.663935347107437 1.389666223140496 15.304961011830752 83.36943347109505 1.3700917447206695 6.276925041612132 57.1699286394013 3.746889708828522 2.5053792211184582 7.561079859345575 1.9356884327878086
user_003 Jogging 1.446047751169e12 -2.6435 -19.58108 2.10702 -3.7025366363636367 -8.384583090909091 -0.17478227272727276 6.98809225 383.4186939664 4.439533280399999 19.870740285575675 10.519545204986324 1.0590366363636368 11.196496909090909 2.2818022727272727 1.7560833388429749 6.586661157024793 1.5315473223140499 1.1215585971604058 125.36154303528228 5.206621611823347 4.519674473767474 57.41535496920656 4.058250858479553 2.1259526038384475 7.577292060439967 2.0145100790215853
user_003 Jogging 1.446047751428e12 -4.02303 -2.79678 1.41725 -4.137994636363636 -8.785204 -0.44999136363636366 16.1847703809 7.8219783684 2.0085975625 5.100524121284008 10.925348616610346 0.11496463636363607 5.988424 1.8672413636363636 1.6297226611570246 7.185217322314048 1.6297223140495867 1.3216867614223074e-2 35.861222003776 3.4865903100745865 5.189255505654557 62.7452396789755 3.459631785679336 2.277993745745268 7.921189284379935 1.86000854451783
user_003 Jumping 1.446047778488e12 0.805325 -10.92069 -1.49509 -3.045865625 -10.45605625 -2.8985725 0.6485483556249999 119.26147007610001 2.2352941081 11.051937049215626 11.40789343159099 3.8511906249999996 0.46463375000000084 1.4034825000000002 1.2694057180059524 2.5898877395833337 0.9794885 14.831669230087888 0.2158845216390633 1.9697631278062506 2.994031096828895 19.251323506625305 2.225648644605465 1.7303268757170984 4.387633018681633 1.491860799339357
user_003 Jumping 1.446047779394e12 -3.75479 -19.19788 -4.9056 -2.7758821818181816 -10.80573090909091 -2.442647 14.098447944099998 368.55859649440004 24.064911359999996 20.167348754819013 11.820595137903576 0.9789078181818183 8.392149090909092 2.4629529999999997 1.600585561983471 5.376877752066116 1.2062982314049586 0.9582605164974878 70.42816636404629 6.066137480208998 3.2206154865366234 39.98863645901631 1.938698124881656 1.7946073349166451 6.323656889728942 1.3923714033553174
user_003 Jumping 1.446047779718e12 -0.574206 -0.535886 -1.18853 -2.692273545454545 -6.2699988181818185 -1.8852610909090906 0.329712530436 0.287173804996 1.4126035609000003 1.4246016623365285 7.945104724247008 2.1180675454545446 5.734112818181819 0.6967310909090905 1.9156986446280988 6.127132917355372 1.4292522892561985 4.48621012710784 32.88004981163704 0.4854342130393713 4.538447186411323 48.73007696745106 2.676119181954217 2.130363158339752 6.980693158093333 1.6358848315068566
user_003 Jumping 1.446047779783e12 -1.72382 -13.98632 -4.10087 -2.5633780909090906 -7.144398818181816 -2.104732 2.9715553923999996 195.61714714239997 16.817134756899996 14.676710710908626 8.766751727191796 0.8395580909090907 6.841921181818183 1.9961379999999997 1.8707269752066111 6.4061433140495865 1.5936175289256198 0.704857788010917 46.81188545821232 3.984566915043999 4.440687567006215 51.69169643753226 3.035135331393246 2.107293896685086 7.189693765212275 1.7421639794787533
user_003 Jumping 1.446047779847e12 -3.14167 -19.58108 -3.48775 -2.5424762727272725 -8.530896999999998 -2.1987910909090904 9.8700903889 383.4186939664 12.1644000625 20.135868106883297 10.050840976414916 0.5991937272727275 11.050183000000002 1.2889589090909097 1.7627335867768592 7.189334537190083 1.6230705206611569 0.3590331228029837 122.10654433348905 1.661415069324828 4.182981353748214 62.25323570487753 3.1015204576431388 2.0452338139558064 7.890071970830021 1.7611134141909028
user_003 Jumping 1.446047780236e12 -4.02303 -9.84772 -0.383802 -2.0791488181818183 -7.405672454545454 -1.9200985454545454 16.1847703809 96.97758919840001 0.147303975204 10.644701196111802 8.261996395467271 1.943881181818182 2.442047545454547 1.5362965454545454 1.4647220413223139 4.597488347107438 1.317141173553719 3.778674049026852 5.963596214260577 2.36020707557557 2.7032977327714196 31.28153946209859 2.150969664053501 1.6441708344242758 5.5929902075811455 1.4666184452861286
user_003 Jumping 1.446047780559e12 -1.8771 1.99325 -1.68669 -2.995352090909091 -8.844426363636364 -2.5123207272727273 3.52350441 3.9730455625 2.8449231561 3.215816090605929 10.297818465970156 1.118252090909091 10.837676363636364 0.8256307272727272 1.4400184710743802 4.84324438016529 1.320624966942149 1.2504877388225537 117.45522896292232 0.6816660978168925 2.8856324564354807 37.52954362327869 2.3031833074215906 1.6987149426656258 6.126136108778411 1.5176242312975865
user_003 Jumping 1.446047782114e12 -3.02671 -6.93538 -2.68302 -2.1592739090909094 -9.077831363636363 -2.784047363636363 9.1609734241 48.0994957444 7.1985963204 8.028640326288132 10.440987932571106 0.8674360909090906 2.142451363636363 0.101027363636363 1.4593388842975203 6.418493917355372 1.5176108347107438 0.7524453718116441 4.590097845547312 1.020652820331392e-2 3.096061633576934 52.39564747111347 2.9478029008267157 1.7595629098094032 7.23848378261038 1.7169166842997117
user_003 Jumping 1.446047783021e12 -1.76214 -12.22358 -5.67201 -2.2951368181818177 -8.046666363636364 -2.6028974545454546 3.1051373796000004 149.4159080164 32.171697440100004 13.590170817031698 9.340850426104161 0.5329968181818177 4.176913636363636 3.0691125454545456 1.147392132231405 5.408547049586778 1.5980530743801653 0.28408560819194156 17.446607525640495 9.41945181666648 2.104757384422643 39.50631767277085 3.717980372110848 1.450778199595873 6.285405131952183 1.9282065169765525
user_003 Jumping 1.446047783474e12 -2.8351 -4.63616 -3.75599 -1.9258677272727274 -6.823900909090909 -2.5610926363636364 8.03779201 21.493979545600002 14.107460880100001 6.6059997302225195 8.171110182376047 0.9092322727272728 2.187740909090909 1.1948973636363638 1.018495512396694 4.917666958677686 1.2648870661157026 0.8267033257688018 4.786210285309917 1.4277797096251326 1.6454297082086164 35.01551621607166 2.5264965598920623 1.2827430406003442 5.917390997396713 1.589495693574557
user_003 Jumping 1.446047783928e12 -1.22565 0.345482 -0.805325 -2.3752596363636367 -6.256064 -2.17789 1.5022179224999999 0.119357812324 0.6485483556249999 1.5066930976310338 7.873185761106638 1.1496096363636368 6.601546 1.3725650000000003 1.3497614297520661 5.791750727272727 1.7095292975206613 1.3216023160201331 43.580409590116 1.8839346792250007 2.7612193103155254 44.96614526686418 5.186450031648668 1.6616917013440025 6.705680074896518 2.2773778851232986
user_003 Jumping 1.446047785158e12 -4.3296 -19.38948 -5.17384 -2.127920545454545 -8.778236090909092 -3.0174524545454546 18.74543616 375.95193467039996 26.768620345600002 20.529636898299003 10.425059342349634 2.201679454545455 10.611243909090907 2.1563875454545456 1.4099344958677689 7.0499873553719 1.8989135371900827 4.847392420567572 112.59849729821889 4.65000724619148 2.826879506975875 61.40943646769882 5.966533630196475 1.6813326580352488 7.836417323477535 2.442648896218299
user_003 Jumping 1.446047785741e12 -5.67081 -19.58108 -6.59169 -2.981417909090909 -11.300410000000001 -3.0104854545454547 32.158086056100004 383.4186939664 43.450377056099995 21.424919068192533 12.213006242934197 2.689392090909091 8.280669999999999 3.581204545454545 1.1746283057851243 4.157596 1.0412976611570246 7.232829818644373 68.56949564889999 12.825025996384296 1.990508293795564 32.482416710883314 2.1246119344821945 1.4108537464229112 5.699334760380664 1.457604862259383
user_003 Jumping 1.446047786065e12 -3.7722e-2 0.230521 0.382604 -2.6016978181818184 -7.217555818181818 -2.3207198181818183 1.422949284e-3 5.3139931441e-2 0.146385820816 0.4482730212058272 8.746920955704528 2.5639758181818184 7.448076818181819 2.7033238181818184 1.274704181818182 5.073483123966943 1.6246536198347108 6.573971996221125 55.4738482895374 7.3079596659491255 2.612508774391919 37.036034654309766 4.703861187725261 1.6163257018286628 6.085723839799976 2.1688386725907627
user_003 Jumping 1.44604778697e12 -0.267643 -8.92803 -1.57173 -2.608665363636364 -10.115963636363636 -3.3309809090909095 7.163277544900001e-2 79.7097196809 2.4703351929000004 9.069271616246201 11.16523610158163 2.341022363636364 1.1879336363636366 1.7592509090909094 1.3396285206611571 3.1194631487603317 1.5305941487603305 5.4803857070455875 1.4111863244041327 3.0949637611371914 2.725388990184014 16.98034189431018 4.198812068786995 1.6508752194469496 4.120721040583818 2.0491003071560443
user_003 Jumping 1.446047787165e12 -0.650847 0.383802 -0.728685 -2.3682926363636363 -7.3185804545454545 -2.703921636363636 0.42360181740899994 0.147303975204 0.530981829225 1.049708350847034 9.016998308071146 1.7174456363636363 7.702382454545455 1.9752366363636358 1.3750980991735537 5.015211049586777 1.9258318429752064 2.9496195138644956 59.32669547608966 3.90155976963313 3.0779925111574262 38.28173155279879 5.6355475934485995 1.7544208477892145 6.187223250602712 2.373930831647923
user_003 Jumping 1.446047788071e12 -0.382604 -7.1653 -1.83997 -2.674855818181818 -10.45388 -3.4598772727272733 0.146385820816 51.34152409 3.3854896009 7.407658166500125 11.447674154518019 2.2922518181818177 3.2885799999999996 1.6199072727272732 0.9424898099173553 3.4890507603305783 1.4162686033057852 5.254418397957849 10.814758416399998 2.6240995722347122 1.5625571828485925 19.114791200640433 3.1594544490391656 1.2500228729301686 4.372046568901163 1.7774854286432746
user_003 Jumping 1.446047788524e12 -1.83878 -13.06663 -4.40743 -2.284686363636364 -9.189309181818182 -3.341432 3.3811118884000004 170.7368195569 19.425439204899998 13.911986581728721 10.958216505306934 0.4459063636363638 3.8773208181818184 1.0659979999999996 1.2341684628099174 7.045554041322314 2.3818761983471077 0.1988324851314051 15.033616727106125 1.136351736003999 2.4039647392245826 56.751996420586586 6.585076787756369 1.5504724245289185 7.533392092582636 2.566140445836192
user_003 Jumping 1.446047788978e12 -4.98104 -15.97897 -4.67568 -2.476287272727273 -9.502837545454547 -3.463361818181818 24.8107594816 255.3274822609 21.861983462399998 17.37815367652444 10.622998855950827 2.5047527272727272 6.4761324545454535 1.2123181818181816 0.6900824132231407 3.6885678677685947 1.235749785123967 6.2737862247801655 41.94029156881692 1.4697153739669417 0.9094025823986598 24.75910834947861 2.8048255856699282 0.9536260181007331 4.975852524892454 1.6747613518558184
user_003 Jumping 1.446047789366e12 -0.689167 0.230521 -0.728685 -2.040828272727273 -7.03640390909091 -2.648185181818182 0.47495115388899994 5.3139931441e-2 0.530981829225 1.029112683118326 8.566401817896816 1.3516612727272732 7.2669249090909105 1.919500181818182 0.9640250082644628 5.1925611652892565 1.7240976033057849 1.826988196190712 52.80819763436594 3.684480948000034 1.8265146617737733 38.92138095737842 4.682598069418348 1.3514860938144253 6.238700261863718 2.163931160970318
user_003 Jumping 1.446047789431e12 -1.14901 -1.95374 -2.72134 -1.953736454545455 -6.5486911818181825 -2.7039233636363638 1.3202239801000002 3.8170999876000002 7.405691395600001 3.5416119724357156 8.169979126377969 0.8047264545454549 4.594951181818183 1.741663636363633e-2 1.0083625371900826 5.557712355371901 1.6746931487603307 0.647584666645298 21.113576363292314 3.0333922222313927e-4 1.8762498455014827 40.81039543439705 4.654028349160952 1.3697626967841847 6.388301451434258 2.1573197141733425
user_003 Jumping 1.446047789495e12 -3.90807 -17.74171 -7.58802 -2.1139846363636363 -7.62514390909091 -3.205570636363637 15.2730111249 314.7682737241 57.578047520400006 19.688050496923253 9.358800088769957 1.7940853636363636 10.116566090909092 4.382449363636363 1.1537262148760332 6.411527363636364 2.0167259669421482 3.2187422920142232 102.34490947173167 19.205862424836763 2.1654029518886837 50.06674660371802 6.36506042608091 1.4715308192113015 7.0757859354080255 2.5229071378235286
user_003 Jumping 1.44604778969e12 -2.79678 -7.7401 -2.37646 -2.023410090909091 -8.565733909090909 -3.4076233636363638 7.8219783684 59.90914801 5.647562131599999 8.566136148229258 10.261278348407382 0.773369909090909 0.8256339090909091 1.031163363636364 1.00804579338843 6.803598520661157 2.0537784710743803 0.5981010162872809 0.6816713518407355 1.06329788250586 1.7002829353981377 55.666831209060064 6.432113881496044 1.303948977298628 7.46102078867631 2.5361612491117445
user_003 Jumping 1.446047790078e12 -5.51753 -19.58108 -7.24314 -2.786332727272727 -10.19608909090909 -3.6131590909090905 30.4431373009 383.4186939664 52.4630770596 21.594557377424987 11.293414580744383 2.7311972727272726 9.38499090909091 3.62998090909091 0.8785175041322314 4.163930198347109 1.2959231239669422 7.459438542552892 88.07805436371902 13.176761400364468 1.3735754555330413 30.66790812930183 3.5983363200454987 1.171996354743922 5.537861331714783 1.8969281272745941
user_003 Jumping 1.446047790984e12 -3.94639 -5.36425 -3.75599 -2.120953181818182 -7.109561 -3.2508581818181814 15.5739940321 28.7751780625 14.107460880100001 7.645693753656368 8.673083640943078 1.8254368181818181 1.745311 0.5051318181818187 1.195847074380165 4.592418834710744 1.636056132231405 3.33221957717376 3.046110486721 0.25515815373967 2.4076508899487554 32.64328227998147 4.830573809957643 1.551660687762874 5.713429992568516 2.1978566399921635
user_003 Jumping 1.446047791114e12 -7.70178 -19.58108 -7.5497 -2.946581363636364 -10.105513 -4.309891818181819 59.317415168400004 383.4186939664 56.997970089999995 22.354732814882848 11.61773381092931 4.7551986363636365 9.475567 3.239808181818181 1.3019401157024795 4.772619710743801 1.4381207768595041 22.611914071274587 89.786369971489 10.496357054976027 3.343503517246846 35.837988853437764 4.02036337652106 1.8285249566923734 5.9864838472543935 2.0050843813967183
user_003 Jumping 1.446047791568e12 -2.87342 -15.97897 -6.28513 -2.3055863636363636 -6.6601691818181825 -3.212538181818182 8.2565424964 255.3274822609 39.5028591169 17.409390680727455 8.421526987651102 0.5678336363636363 9.318800818181817 3.0725918181818175 1.446669702479339 6.484367231404959 2.031925909090909 0.3224350385859503 86.8400486889461 9.440820481157846 3.813957346986096 49.52847432526871 5.995280938105451 1.9529355716423664 7.037646930989697 2.448526278826807
user_003 Jumping 1.446047791827e12 -2.29862 -6.55217 -2.49142 -1.9641881818181817 -7.280260090909092 -3.181184545454545 5.2836539044 42.930931708900005 6.207173616400001 7.377110493255472 8.788531724429772 0.33443181818181844 0.7280900909090917 0.6897645454545449 1.1261739999999998 6.306384099173553 2.2213108677685947 0.11184464101239687 0.5301151804800094 0.47577512816611495 1.9109248881675382 50.99588928979224 6.629910003092078 1.3823620684059361 7.141140615461387 2.5748611619060315
user_003 Jumping 1.446047792344e12 -3.10335 3.14286 -1.26517 -2.953548181818182 -8.90713090909091 -3.501681181818181 9.6307812225 9.877568979600001 1.6006551288999997 4.594453757629953 10.941628346937778 0.14980181818181793 12.04999090909091 2.236511181818181 0.999812826446281 3.8237974793388427 1.812455851239669 2.2440584730578434e-2 145.2022809091736 5.001982266397757 1.8885143120312426 26.270304023665425 5.8452978576593555 1.374232262767558 5.12545646978544 2.4177050807861895
user_003 Jumping 1.446047793057e12 -3.40991 -4.5212 -3.18118 -2.462351909090909 -7.2071046363636375 -3.024419818181818 11.6274862081 20.441249440000004 10.1199061924 6.495278426711207 8.674293689934707 0.9475580909090908 2.685904636363637 0.15676018181818208 1.0726530165289256 4.93793579338843 1.4678901570247935 0.8978663356472808 7.214083715639682 2.4573754603669503e-2 1.72976303665992 39.52732881159241 3.1747400768519607 1.3152045607660885 6.287076332572431 1.7817800304336
user_003 Jumping 1.446047793186e12 -4.7128 -14.7144 -2.75966 -3.2113400000000003 -8.840943 -3.634060909090908 22.21048384 216.51356736 7.615723315599999 15.695215019731332 10.612367757126883 1.5014599999999994 5.873457 0.8744009090909084 0.7952270578512397 4.540798404958678 1.071702603305785 2.254382131599998 34.497497130849 0.764576949819007 0.7830567086205332 34.99927923490504 1.741114685615601 0.8849049150165984 5.916018867017332 1.3195130486719717
user_003 Jumping 1.446047793575e12 -0.995729 0.383802 -0.537083 -2.7410467272727272 -6.395409181818182 -2.8885566363636364 0.991476241441 0.147303975204 0.28845814888899995 1.1946708189011732 8.27717575135295 1.7453177272727274 6.779211181818182 2.3514736363636364 1.2639381239669418 5.466502917355373 1.8156223140495868 3.0461339691324385 45.957704247688675 5.529428262513223 1.9598808640562646 39.03616815904957 4.99167780265026 1.3999574508020822 6.247893097600948 2.2342063026162693
user_003 Jumping 1.446047794481e12 -1.95374 3.41111 -1.07357 -2.643502818181818 -9.126602727272727 -3.428524545454545 3.8170999876000002 11.635671432099999 1.1525525448999998 4.0749630629737 10.79227622301754 0.6897628181818181 12.537712727272726 2.354954545454545 1.1131894545454544 3.9501590082644626 1.6290873471074379 0.4757727453461238 157.19424043161652 5.545810911157022 1.8705564207425525 31.301814921364322 3.736381640676486 1.3676828655585886 5.594802491720715 1.932972229670278
user_003 Sitting 1.44604752743e12 0.460442 -0.689167 9.38788 0.46044236363636365 -0.657814 9.443618181818183 0.21200683536400003 0.47495115388899994 88.13229089439999 9.424396473178163 9.478071981030691 3.6363636363168084e-7 3.1352999999999964e-2 5.573818181818346e-2 3.4032538370720185e-2 4.40547175127902e-2 2.419795146267877e-2 1.3223140495527202e-13 9.830106089999977e-4 3.1067449123968775e-3 2.3483069980241396e-3 3.906631446102497e-3 8.135460878653428e-4 4.845933344593319e-2 6.250305149432703e-2 2.8522729320058816e-2
user_003 Sitting 1.446047527559e12 0.460442 -0.612526 9.50284 0.453475 -0.6578139090909091 9.44710181818182 0.21200683536400003 0.375188100676 90.30396806560002 9.53368569870226 9.481220909788886 6.967000000000001e-3 4.528790909090907e-2 5.573818181818169e-2 3.4032455037386854e-2 5.070537866981499e-2 2.7628805457168906e-2 4.8539089000000014e-5 2.0509947098264446e-3 3.1067449123966797e-3 2.308586652947751e-3 4.1636947314954344e-3 1.065423617408702e-3 4.804775388036106e-2 6.452669781954935e-2 3.264082746207121e-2
user_003 Sitting 1.446047528402e12 0.422122 -0.689167 9.46452 0.4569586363636363 -0.6752324545454544 9.457552727272725 0.178186982884 0.47495115388899994 89.5771388304 9.498961888920968 9.492741447006507 3.483663636363632e-2 1.3934545454545533e-2 6.967272727274931e-3 3.166966115702479e-2 2.5652355371900804e-2 2.565223140495896e-2 1.2135912331322283e-3 1.9417155702479557e-4 4.854288925622906e-5 1.4210099365206607e-3 7.910343762411707e-4 1.09331825574758e-3 3.769628544725144e-2 2.8125333353423043e-2 3.3065363384478026e-2
user_003 Sitting 1.446047529049e12 0.460442 -0.650847 9.46452 0.4639258181818182 -0.657814 9.436650909090908 0.21200683536400003 0.42360181740899994 89.5771388304 9.49803913885245 9.471062812864579 3.483818181818199e-3 6.9670000000000565e-3 2.786909090909262e-2 2.0901991735537175e-2 3.3886504132231404e-2 2.565223140495896e-2 1.2136989123967062e-5 4.853908900000079e-5 7.766862280992689e-4 1.0150072620518401e-3 1.7045471308392188e-3 1.0182974268970488e-3 3.1859178615460886e-2 4.128616149315917e-2 3.191077289720587e-2
user_003 Sitting 1.446047529689e12 0.383802 -0.689167 9.50284 0.45347518181818175 -0.7031015454545454 9.45406909090909 0.147303975204 0.47495115388899994 90.30396806560002 9.53552427476817 9.4913909847906 6.967318181818177e-2 1.3934545454545422e-2 4.877090909091031e-2 4.687115702479338e-2 4.972128099173549e-2 4.6237355371901406e-2 4.854352264669415e-3 1.9417155702479248e-4 2.378601573553838e-3 3.2568331531720477e-3 3.7676414018760294e-3 2.8993343855747643e-3 5.7068670504682756e-2 6.138111600383321e-2 5.3845467641898744e-2
user_003 Sitting 1.44604752981e12 0.422122 -0.765807 9.54116 0.4325731818181817 -0.710068909090909 9.499356363636364 0.178186982884 0.586460361249 91.0337341456 9.58114719069345 9.536678617258826 1.045118181818172e-2 5.573809090909099e-2 4.180363636363538e-2 8.170777685950412e-2 7.790738016528927e-2 6.587239669421473e-2 1.0922720139669217e-4 3.1067347781900912e-3 1.747544013223058e-3 9.461612793580014e-3 9.708744318026302e-3 7.735971351465028e-3 9.72708219024596e-2 9.853296056663628e-2 8.795437084912283e-2
user_003 Sitting 1.446047529883e12 0.460442 -0.650847 9.46452 0.47786072727272727 -0.6926505454545453 9.454069090909089 0.21200683536400003 0.42360181740899994 89.5771388304 9.49803913885245 9.492320128700781 1.7418727272727252e-2 4.180354545454534e-2 1.0450909090911509e-2 5.130488429752066e-2 9.279201652892563e-2 6.17553719008262e-2 3.034120598016522e-4 1.7475364125702387e-3 1.0922150082649681e-4 3.8283135643719006e-3 1.240843196444929e-2 5.813010988429641e-3 6.1873367165299e-2 0.11139314146054635 7.624310453037468e-2
user_003 Sitting 1.446047529891e12 0.575403 -0.765807 9.50284 0.49876263636363627 -0.678715818181818 9.44710181818182 0.331088612409 0.586460361249 90.30396806560002 9.550995604608874 9.485168028171538 7.664036363636373e-2 8.709118181818198e-2 5.573818181818169e-2 5.130490082644628e-2 8.139087603305785e-2 5.8588429752066e-2 5.873745338314064e-3 7.584873950487632e-3 3.1067449123966797e-3 3.828316097934638e-3 8.9927083140571e-3 5.34964704552959e-3 6.187338763907014e-2 9.482989145863818e-2 7.314128140475522e-2
user_003 Sitting 1.44604753002e12 0.537083 -0.689167 9.50284 0.4778606363636364 -0.6403956363636364 9.502839999999999 0.28845814888899995 0.47495115388899994 90.30396806560002 9.542922894395511 9.537381176494081 5.922236363636357e-2 4.8771363636363585e-2 1.7763568394002505e-15 8.519144628099172e-2 8.424148760330576e-2 4.687074380165254e-2 3.5072883546776786e-3 2.3786459109504084e-3 3.1554436208840472e-30 1.0087168569314048e-2 1.018977686158527e-2 2.9942136691209083e-3 0.10043489716883294 0.10094442461862502 5.471940852312741e-2
user_003 Sitting 1.446047530142e12 0.575403 -0.765807 9.46452 0.3629000909090909 -0.6891668181818181 9.447101818181817 0.331088612409 0.586460361249 89.5771388304 9.512869588302891 9.479941573052752 0.21250290909090908 7.664018181818189e-2 1.7418181818182887e-2 0.10039261983471072 6.618949586776866e-2 7.47398347107437e-2 4.515748637209917e-2 5.873717469123977e-3 3.0339305785127694e-4 1.3279947553097667e-2 6.071256061773864e-3 7.888219504132201e-3 0.11523865476955927 7.791826526414627e-2 8.881564898221597e-2
user_003 Sitting 1.446047530158e12 0.498763 -0.535886 9.50284 0.3698673636363637 -0.6647811818181817 9.461036363636364 0.24876453016900002 0.287173804996 90.30396806560002 9.530997135702277 9.492622848471019 0.12889563636363632 0.12889518181818171 4.180363636363715e-2 9.849253719008265e-2 8.202431404958684e-2 7.125619834710778e-2 1.6614085073586766e-2 1.6613967895942123e-2 1.7475440132232066e-3 1.29092732124846e-2 8.401342212178822e-3 7.35314447483098e-3 0.11361898262387583 9.165883597438286e-2 8.575047798602047e-2
user_003 Sitting 1.446047530198e12 0.422122 -0.535886 9.57948 0.43257318181818183 -0.6403957272727271 9.44361818181818 0.178186982884 0.287173804996 91.7664370704 9.603738743753913 9.47623788966848 1.0451181818181832e-2 0.10450972727272712 0.13586181818181942 9.057513223140498e-2 7.094005785123968e-2 5.7955041322314375e-2 1.0922720139669449e-4 1.0922283094619801e-2 1.8458433639669758e-2 1.2908162652194589e-2 7.0321907231382375e-3 4.926000012021046e-3 0.11361409530597244 8.385815835765914e-2 7.018546866710407e-2
user_003 Sitting 1.446047530247e12 0.307161 -0.880768 9.38788 0.46740963636363625 -0.6264609999999999 9.457552727272729 9.434787992100001e-2 0.775752269824 88.13229089439999 9.43410785629171 9.490591922281 0.16024863636363623 0.25430700000000006 6.967272727272977e-2 7.505712396694214e-2 6.048910743801654e-2 5.067107438016582e-2 2.5679625456404915e-2 6.467205024900004e-2 4.854288925620183e-3 9.283960639918853e-3 9.24644060953268e-3 4.152623526371195e-3 9.635331151506342e-2 9.615841413798731e-2 6.444085293019634e-2
user_003 Sitting 1.446047530328e12 0.345482 -0.574206 9.4262 0.39773645454545453 -0.6473628181818181 9.429683636363636 0.119357812324 0.329712530436 88.85324643999999 9.449990305961165 9.461319720505479 5.225445454545452e-2 7.315681818181807e-2 3.4836363636365775e-3 8.392447107438017e-2 8.550806611570248e-2 5.320462809917343e-2 2.7305280198429726e-3 5.351920046487588e-3 1.2135722314051077e-5 9.723052447205857e-3 1.2296954448760333e-2 5.915613004357611e-3 9.86055396375166e-2 0.1108916338086888 7.691302233274681e-2
user_003 Sitting 1.446047530441e12 0.498763 -0.612526 9.46452 0.5057300909090908 -0.657813909090909 9.429683636363636 0.24876453016900002 0.375188100676 89.5771388304 9.49742551754132 9.466509525966101 6.967090909090812e-3 4.528790909090896e-2 3.4836363636364e-2 6.492278512396693e-2 5.3521900826446235e-2 4.402049586776839e-2 4.8540355735535836e-5 2.0509947098264346e-3 1.213572231404984e-3 5.985194005758828e-3 4.486975113920357e-3 4.422919159729566e-3 7.736403560931157e-2 6.698488720540147e-2 6.650503108584768e-2
user_003 Sitting 1.446047530514e12 0.498763 -0.880768 9.31124 0.4499914545454546 -0.7031015454545453 9.440134545454544 0.24876453016900002 0.775752269824 86.6991903376 9.366093483282825 9.477717120133109 4.877154545454543e-2 0.1776664545454547 0.12889454545454448 6.2389223140495845e-2 6.713966942148762e-2 6.650578512396682e-2 2.378663646024791e-3 3.156536907075212e-2 1.6613803847933633e-2 6.276472416003001e-3 7.296975301380921e-3 6.55549654455295e-3 7.922419085104626e-2 8.542233490944227e-2 8.096602092577447e-2
user_003 Sitting 1.446047530659e12 0.383802 -0.727487 9.54116 0.4465077272727273 -0.6821995454545454 9.461036363636365 0.147303975204 0.529237335169 91.0337341456 9.576548201516713 9.496631596516915 6.270572727272733e-2 4.5287454545454575e-2 8.012363636363418e-2 5.9222214876033095e-2 6.08056611570248e-2 6.428892561983478e-2 3.93200823280166e-3 2.050953539206614e-3 6.419797104131881e-3 4.411940753462063e-3 4.693273525742299e-3 5.314343126070618e-3 6.642244164032261e-2 6.850747058345023e-2 7.289954132963128e-2
user_003 Sitting 1.446047531194e12 0.575403 -0.612526 9.46452 0.4360567272727273 -0.675232090909091 9.485421818181818 0.331088612409 0.375188100676 89.5771388304 9.501758550051933 9.52033802670179 0.13934627272727268 6.270609090909096e-2 2.090181818181769e-2 0.10197616528925618 5.8272214876033075e-2 4.9404297520661444e-2 1.9417383722983458e-2 3.93205383709918e-3 4.368860033057645e-4 1.2546293734994739e-2 5.233893928461308e-3 3.038343568444816e-3 0.11201023942030808 7.23456559059444e-2 5.512117168969484e-2
user_003 Sitting 1.446047531283e12 0.307161 -0.689167 9.50284 0.4325730909090908 -0.6508465454545456 9.44710181818182 9.434787992100001e-2 0.47495115388899994 90.30396806560002 9.532747090918232 9.480348281329318 0.12541209090909078 3.832045454545441e-2 5.573818181818169e-2 8.709162809917352e-2 7.284034710743799e-2 5.162115702479333e-2 1.572819254619005e-2 1.4684572365702374e-3 3.1067449123966797e-3 1.0321058878268211e-2 7.570614707637863e-3 3.4983977688955513e-3 0.1015926123213111 8.700927943408027e-2 5.9147254956553576e-2
user_003 Sitting 1.446047531348e12 0.613724 -0.765807 9.57948 0.46044236363636365 -0.7379379999999999 9.443618181818183 0.37665714817600005 0.586460361249 91.7664370704 9.629618610299424 9.484515872090418 0.1532816363636364 2.7869000000000144e-2 0.13586181818181764 8.424131404958675e-2 6.713955371900827e-2 6.777256198347087e-2 2.349526004631406e-2 7.76681161000008e-4 1.8458433639669276e-2 1.073036766763711e-2 7.2020997030293e-3 5.847211660405668e-3 0.10358748798786999 8.48651854592288e-2 7.646706258517891e-2
user_003 Sitting 1.446047531509e12 0.383802 -0.650847 9.46452 0.4499912727272728 -0.6543302727272727 9.440134545454542 0.147303975204 0.42360181740899994 89.5771388304 9.494632411158054 9.473935581651636 6.618927272727282e-2 3.4832727272727793e-3 2.438545454545782e-2 4.940451239669421e-2 4.307072727272726e-2 5.193785123966954e-2 4.381019824165301e-3 1.2133188892562345e-5 5.946503933885893e-4 4.212253419712996e-3 3.38923822532682e-3 4.649084893764115e-3 6.490187531738198e-2 5.821716435319415e-2 6.818419827030392e-2
user_003 Sitting 1.446047531598e12 0.498763 -0.689167 9.46452 0.4813443636363637 -0.6856832727272727 9.468003636363637 0.24876453016900002 0.47495115388899994 89.5771388304 9.502676176449349 9.506151313791571 1.741863636363633e-2 3.4837272727272772e-3 3.4836363636365775e-3 8.740837190082638e-2 5.605531404958677e-2 7.062280991735535e-2 3.034088927685939e-4 1.2136355710743833e-5 1.2135722314051077e-5 1.465137964412546e-2 5.0474440098054055e-3 7.304601585574778e-3 0.12104288349227914 7.104536585735487e-2 8.54669619535805e-2
user_003 Sitting 1.44604753193e12 0.460442 -0.880768 9.46452 0.4534750909090908 -0.7170360909090907 9.422716363636363 0.21200683536400003 0.775752269824 89.5771388304 9.516559143702517 9.461390118682749 6.96690909090919e-3 0.1637319090909093 4.180363636363715e-2 3.0719553719008252e-2 7.727383471074388e-2 5.320462809917359e-2 4.853782228099312e-5 2.6808138054553784e-2 1.7475440132232066e-3 1.446381519275732e-3 9.485840937113464e-3 5.361782767843718e-3 3.8031322870441045e-2 9.7395281903763e-2 7.322419523520704e-2
user_003 Sitting 1.446047531955e12 0.422122 -0.650847 9.65612 0.47786072727272727 -0.6996178181818181 9.44710181818182 0.178186982884 0.42360181740899994 93.24065345439999 9.687230886826894 9.485730492495545 5.573872727272727e-2 4.8770818181818165e-2 0.20901818181818044 4.402082644628096e-2 8.455785123966947e-2 6.808925619834692e-2 3.106805717983471e-3 2.3785927061239654e-3 4.3688600330577934e-2 4.2089599727242605e-3 1.0109191196815939e-2 7.540696546957087e-3 6.487649784570881e-2 0.10054447372588877 8.68371841261397e-2
user_003 Sitting 1.446047532084e12 0.383802 -0.650847 9.46452 0.47786072727272716 -0.6612976363636363 9.450585454545456 0.147303975204 0.42360181740899994 89.5771388304 9.494632411158054 9.486668111432918 9.405872727272718e-2 1.0450636363636301e-2 1.3934545454544534e-2 6.555622314049582e-2 7.949095041322311e-2 7.03061157024793e-2 8.847044176165272e-3 1.0921580040495736e-4 1.941715570247677e-4 5.857225243961679e-3 1.1110982175987227e-2 7.402790611570207e-3 7.65325110261102e-2 0.10540864374417891 8.603947124180975e-2
user_003 Sitting 1.446047532165e12 0.422122 -0.574206 9.54116 0.44650772727272725 -0.6369118181818182 9.433167272727273 0.178186982884 0.329712530436 91.0337341456 9.567739213571825 9.465520089793488 2.4385727272727253e-2 6.27058181818182e-2 0.1079927272727268 5.953895867768593e-2 3.927050413223136e-2 6.808925619834726e-2 5.946636946198338e-4 3.932019633851242e-3 1.166242914380155e-2 4.64693759204658e-3 2.0719397710060082e-3 7.326666535236722e-3 6.816845012208052e-2 4.551856512463907e-2 8.559594929222247e-2
user_003 Sitting 1.446047532214e12 0.537083 -0.612526 9.54116 0.463926 -0.6299445454545455 9.443618181818179 0.28845814888899995 0.375188100676 91.0337341456 9.575874915388411 9.47633465958667 7.315699999999997e-2 1.7418545454545464e-2 9.754181818182062e-2 4.655442975206612e-2 4.655447107438018e-2 4.9087603305785876e-2 5.351946648999996e-3 3.0340572575206643e-4 9.514406294215351e-3 3.2932427384973705e-3 2.640112742971452e-3 3.719047265514745e-3 5.7386781914456314e-2 5.1382027431500325e-2 6.09839918791378e-2
user_003 Sitting 1.446047532391e12 0.460442 -0.535886 9.4262 0.41863836363636364 -0.6299446363636363 9.461036363636364 0.21200683536400003 0.287173804996 88.85324643999999 9.452641275345213 9.492556516525278 4.1803636363636376e-2 9.405863636363632e-2 3.4836363636364e-2 9.659232231404959e-2 8.835825619834711e-2 6.49223140495872e-2 1.7475440132231415e-3 8.847027074586768e-3 1.213572231404984e-3 1.4473703594323817e-2 1.2876186440492116e-2 5.98070460586029e-3 0.12030670635639484 0.11347328514012502 7.733501539315997e-2
user_003 Sitting 1.446047532529e12 0.498763 -0.689167 9.34956 0.42560581818181814 -0.6752322727272727 9.481938181818181 0.24876453016900002 0.47495115388899994 87.41427219360001 9.388183417342145 9.515991487267254 7.315718181818187e-2 1.3934727272727265e-2 0.13237818181818106 7.98074214876033e-2 4.908786776859502e-2 6.808925619834709e-2 5.3519732515785205e-3 1.9417662416528904e-4 1.7523983021487402e-2 7.866250311642372e-3 3.222624247057847e-3 6.397732154470296e-3 8.869188413627467e-2 5.676816226599067e-2 7.998582470957148e-2
user_003 Sitting 1.446047532691e12 0.422122 -0.842448 9.46452 0.42908936363636363 -0.6787158181818183 9.405298181818182 0.178186982884 0.7097186327039999 89.5771388304 9.511311394649425 9.440375723637255 6.967363636363633e-3 0.16373218181818172 5.9221818181818264e-2 4.497070247933884e-2 8.96250743801653e-2 6.048859504132167e-2 4.854415604132226e-5 2.6808227362942118e-2 3.5072237487603405e-3 3.0802885080879025e-3 1.2626851415314798e-2 4.959097436513814e-3 5.550034691862658e-2 0.11236926365921776 7.042085938494229e-2
user_003 Sitting 1.446047532788e12 0.460442 -0.689167 9.54116 0.4360566363636364 -0.6403957272727273 9.436650909090908 0.21200683536400003 0.47495115388899994 91.0337341456 9.57709205003549 9.468743163819505 2.438536363636362e-2 4.877127272727266e-2 0.104509090909092 4.117052066115701e-2 5.162148760330579e-2 5.35213223140498e-2 5.946459596776853e-4 2.3786370434380104e-3 1.0922150082644855e-2 2.744925226993988e-3 4.317064291385426e-3 4.449397099323852e-3 5.239203400321454e-2 6.570437041312721e-2 6.670380123594045e-2
user_003 Sitting 1.446047532804e12 0.230521 -0.574206 9.54116 0.41167100000000006 -0.636912 9.454069090909092 5.3139931441e-2 0.329712530436 91.0337341456 9.561202152840247 9.484988102207778 0.18115000000000006 6.270600000000004e-2 8.709090909090733e-2 5.225482644628099e-2 6.0805702479338856e-2 6.017190082644642e-2 3.281532250000002e-2 3.932042436000005e-3 7.584826446280685e-3 5.409289030824944e-3 5.143409093702481e-3 5.145546261157036e-3 7.354786897541589e-2 7.171756475022337e-2 7.173246309138587e-2
user_003 Sitting 1.446047532819e12 0.575403 -0.727487 9.34956 0.42212199999999994 -0.6403956363636364 9.450585454545454 0.331088612409 0.529237335169 87.41427219360001 9.395456249761265 9.482358118978716 0.15328100000000006 8.70913636363636e-2 0.10102545454545364 6.55560991735537e-2 7.157335537190083e-2 6.935603305785162e-2 2.349506496100002e-2 7.584905620041317e-3 1.0206142466115519e-2 7.620226583552217e-3 6.374646010217882e-3 6.172669667918895e-3 8.729390920076965e-2 7.984138031257904e-2 7.856633928037436e-2
user_003 Sitting 1.446047532991e12 0.498763 -0.689167 9.46452 0.41863836363636364 -0.6578139090909092 9.408781818181819 0.24876453016900002 0.47495115388899994 89.5771388304 9.502676176449349 9.441844129998412 8.012463636363637e-2 3.1353090909090775e-2 5.573818181818169e-2 7.474038842975207e-2 6.58730082644628e-2 6.080528925619788e-2 6.41995735240496e-3 9.830163095537105e-4 3.1067449123966797e-3 6.935123140278735e-3 7.1513644837716e-3 6.11640404628094e-3 8.327738672820333e-2 8.456574060322301e-2 7.820744239700554e-2
user_003 Sitting 1.446047533055e12 0.460442 -0.650847 9.38788 0.42212200000000005 -0.6578139090909091 9.415749090909088 0.21200683536400003 0.42360181740899994 88.13229089439999 9.421671802136444 9.44895072125354 3.8319999999999965e-2 6.966909090909135e-3 2.7869090909089067e-2 7.695723966942146e-2 6.523954545454545e-2 5.225454545454495e-2 1.4684223999999974e-3 4.8537822280992347e-5 7.766862280990708e-4 7.050963895679938e-3 7.138123901706236e-3 4.8355337184071805e-3 8.397001783779695e-2 8.448741860008646e-2 6.953800197307354e-2
user_003 Sitting 1.446047533249e12 0.345482 -0.650847 9.46452 0.4256057272727272 -0.6578139090909091 9.436650909090908 0.119357812324 0.42360181740899994 89.5771388304 9.493160614891808 9.46968381931937 8.012372727272721e-2 6.966909090909135e-3 2.786909090909262e-2 5.85887603305785e-2 5.953899173553718e-2 4.4337190082644765e-2 6.419811672074369e-3 4.8537822280992347e-5 7.766862280992689e-4 4.047867299084896e-3 6.319509547628849e-3 3.5458374106687065e-3 6.362285201941906e-2 7.949534293044372e-2 5.954693451949233e-2
user_003 Sitting 1.446047533443e12 0.498763 -0.650847 9.4262 0.4499913636363637 -0.657814 9.4262 0.24876453016900002 0.42360181740899994 88.85324643999999 9.461797545264748 9.460293159957134 4.8771636363636295e-2 6.9670000000000565e-3 0.0 5.193814049586775e-2 5.257162809917355e-2 2.660231404958728e-2 2.37867251358677e-3 4.853908900000079e-5 0.0 3.3914277338775323e-3 5.8075949420383155e-3 9.09075926070645e-4 5.823596598217919e-2 7.620757798302158e-2 3.0150885991470384e-2
user_003 Sitting 1.446047533702e12 0.422122 -0.650847 9.50284 0.4395403636363637 -0.6612977272727272 9.443618181818183 0.178186982884 0.42360181740899994 90.30396806560002 9.534451052152557 9.477111134477436 1.7418363636363676e-2 1.0450727272727223e-2 5.9221818181818264e-2 3.4203123966942146e-2 2.786920661157025e-2 2.72357024793389e-2 3.0339939176859645e-4 1.0921770052892458e-4 3.5072237487603405e-3 1.6593011434440253e-3 1.3106807544943642e-3 1.1385514025544887e-3 4.073452029230276e-2 3.62033251856009e-2 3.374242733643341e-2
user_003 Sitting 1.44604753435e12 0.498763 -0.689167 9.50284 0.45695872727272735 -0.6612978181818181 9.468003636363635 0.24876453016900002 0.47495115388899994 90.30396806560002 9.540842926579288 9.502149947082033 4.180427272727266e-2 2.7869181818181876e-2 3.4836363636365775e-2 2.945285950413223e-2 1.8051628099173548e-2 3.293619834710784e-2 1.7475972182561929e-3 7.766912952148793e-4 1.2135722314051078e-3 1.126443607117956e-3 5.284638936957168e-4 1.4783516273478882e-3 3.356253278758854e-2 2.2988342560865863e-2 3.844933845136855e-2
user_003 Sitting 1.446047534415e12 0.498763 -0.650847 9.46452 0.4639260909090909 -0.6612978181818181 9.46452 0.24876453016900002 0.42360181740899994 89.5771388304 9.499973956699987 9.499015665677254 3.4836909090909085e-2 1.0450818181818144e-2 0.0 3.103636363636363e-2 1.805163636363636e-2 2.7552396694215275e-2 1.213610235008264e-3 1.0921960066942071e-4 0.0 1.2091900474124713e-3 5.284640664357619e-4 1.1595131047333121e-3 3.477341006304201e-2 2.2988346317988205e-2 3.405162411300395e-2
user_003 Sitting 1.446047534803e12 0.422122 -0.612526 9.46452 0.4604424545454545 -0.6543305454545453 9.471487272727273 0.178186982884 0.375188100676 89.5771388304 9.493709175762653 9.505321529005101 3.832045454545452e-2 4.180454545454526e-2 6.967272727273155e-3 3.4520173553719e-2 1.3301223140495816e-2 2.026842975206639e-2 1.4684572365702459e-3 1.7476200206611408e-3 4.854288925620431e-5 1.6096746418955666e-3 3.4863351750112544e-4 9.443798455296936e-4 4.012075076435592e-2 1.8671730436709003e-2 3.0730763829259006e-2
user_003 Sitting 1.446047534998e12 0.460442 -0.689167 9.4262 0.453475 -0.6543305454545454 9.461036363636362 0.21200683536400003 0.47495115388899994 88.85324643999999 9.462568595748884 9.494558667728176 6.967000000000001e-3 3.483645454545459e-2 3.483636363636222e-2 2.8502826446280997e-2 1.3934595041322238e-2 1.8051570247933538e-2 4.8539089000000014e-5 1.2135785652975235e-3 1.2135722314048601e-3 1.319511052964688e-3 3.7069852473929204e-4 5.924438984222251e-4 3.632507471382114e-2 1.9253532785940664e-2 2.434017046822444e-2
user_003 Sitting 1.446047535515e12 0.460442 -0.650847 9.46452 0.44650754545454546 -0.6647814545454546 9.440134545454544 0.21200683536400003 0.42360181740899994 89.5771388304 9.49803913885245 9.474119764777338 1.3934454545454555e-2 1.3934454545454611e-2 2.4385454545456042e-2 2.4068826446280996e-2 2.0585239669421454e-2 3.008595041322305e-2 1.941690234793391e-4 1.9416902347934065e-4 5.946503933885027e-4 8.980566373538687e-4 6.454093360931615e-4 1.2367404285499523e-3 2.9967593119132354e-2 2.5404907716682648e-2 3.516732046303716e-2
user_003 Sitting 1.446047537963e12 2.91294 -4.06135 -17.7429 0.6694618181818182 -0.9748269999999999 7.00156909090909 8.485219443599998 16.4945638225 314.81050041 18.433401305133568 10.321177531628067 2.2434781818181815 3.086523 24.74446909090909 0.2169370991735537 0.3173297520661157 2.264065123966943 5.033194352294213 9.526624229529 612.2887505909553 0.457926093891347 0.8678727182561082 55.66323812816227 0.6767023672866432 0.9315968646663149 7.4607799946226985
user_003 Sitting 1.446047537996e12 0.498763 -0.574206 9.38788 0.7008150000000001 -0.9574087272727273 7.0050527272727265 0.24876453016900002 0.329712530436 88.13229089439999 9.418639389795374 10.325252885713102 0.20205200000000006 0.3832027272727273 2.3828272727272726 0.2704586033057851 0.4246898595041323 3.15778347107438 4.082501070400003e-2 0.14684433018925622 5.677865811652892 0.467783707752465 0.9122205865213097 57.8899981788081 0.6839471527482697 0.9551023958305778 7.608547704970253
user_003 Sitting 1.446047538044e12 0.422122 -0.919089 9.50284 0.6903640909090908 -0.9643761818181816 7.005052727272727 0.178186982884 0.8447245899210001 90.30396806560002 9.556509804233187 10.325897387815681 0.2682420909090908 4.528718181818159e-2 2.4977872727272734 0.41043860330578513 0.5539019090909091 4.500261157024793 7.195381933528093e-2 2.050928837033037e-3 6.2389412597983505 0.5123842726084574 0.959901352937687 61.24369845327421 0.7158102210840925 0.9797455552018018 7.825835319841213
user_003 Sitting 1.446047538101e12 0.383802 -0.804128 9.54116 0.443024 -0.7135526363636363 9.461036363636364 0.147303975204 0.646621840384 91.0337341456 9.582674989854764 9.498997704798395 5.9222e-2 9.057536363636365e-2 8.012363636363595e-2 0.13871318181818182 0.11464396694214875 0.9611742148760336 3.5072452839999997e-3 8.203896497859506e-3 6.419797104132165e-3 3.661955596183396e-2 2.796989594571301e-2 2.23701703117055 0.1913623681966597 0.1672420280483139 1.4956660827773525
user_003 Sitting 1.446047538125e12 0.498763 -0.804128 9.57948 0.47089336363636375 -0.7240035454545454 9.450585454545454 0.24876453016900002 0.646621840384 91.7664370704 9.626101154722663 9.490513908927614 2.7869636363636263e-2 8.012445454545458e-2 0.12889454545454626 6.175596694214875e-2 5.922220661157021e-2 0.3128957024793395 7.767166310413167e-4 6.419928216206618e-3 1.661380384793409e-2 8.979472165661901e-3 4.764998892272725e-3 0.5763698394401208 9.476007685550862e-2 6.902897139804942e-2 0.7591902524664821
user_003 Sitting 1.446047538262e12 0.537083 -0.612526 9.50284 0.5092136363636363 -0.6543302727272726 9.485421818181818 0.28845814888899995 0.375188100676 90.30396806560002 9.537694391998782 9.522259443822756 2.786936363636372e-2 4.180427272727261e-2 1.7418181818182887e-2 7.220681818181819e-2 6.175582644628099e-2 6.1121983471074254e-2 7.767014294958724e-4 1.7475972182561883e-3 3.0339305785127694e-4 9.30272696033734e-3 5.69174341896544e-3 5.808597998497331e-3 9.645064520436004e-2 7.544364399315187e-2 7.621415877970006e-2
user_003 Sitting 1.446047538343e12 0.537083 -0.574206 9.38788 0.4743770909090909 -0.7065851818181819 9.471487272727272 0.28845814888899995 0.329712530436 88.13229089439999 9.420746338466236 9.51013996484748 6.270590909090906e-2 0.13237918181818187 8.360727272727253e-2 4.782130578512396e-2 6.175572727272729e-2 5.5104793388430065e-2 3.932031034917352e-3 1.7524247778851254e-2 6.990176052892529e-3 3.4631438297821194e-3 5.9587168723117985e-3 3.907702585123994e-3 5.88484819666754e-2 7.719272551420761e-2 6.251161960087095e-2
user_003 Sitting 1.446047538481e12 0.422122 -0.650847 9.50284 0.42908945454545455 -0.7031014545454545 9.415749090909092 0.178186982884 0.42360181740899994 90.30396806560002 9.534451052152557 9.452520129048189 6.9674545454545544e-3 5.225445454545452e-2 8.709090909090911e-2 5.162155371900827e-2 7.600711570247934e-2 8.075702479338903e-2 4.854542284297533e-5 2.7305280198429726e-3 7.584826446280995e-3 5.528469067821186e-3 9.055587604848238e-3 7.911387701277323e-3 7.435367555017833e-2 9.516085121964935e-2 8.894598192879385e-2
user_003 Sitting 1.446047538554e12 0.422122 -0.535886 9.4262 0.4465077272727273 -0.6647812727272726 9.422716363636363 0.178186982884 0.287173804996 88.85324643999999 9.450852195854086 9.457397142342744 2.438572727272731e-2 0.12889527272727264 3.4836363636365775e-3 4.940454545454545e-2 7.474023140495865e-2 5.637157024793347e-2 5.946636946198365e-4 1.6613991331437993e-2 1.2135722314051077e-5 4.071027434838466e-3 8.259009006755818e-3 4.830017480991683e-3 6.380460355521744e-2 9.087909004141612e-2 6.94983271812472e-2
user_003 Sitting 1.446047538586e12 0.345482 -0.689167 9.4262 0.4465078181818182 -0.6299447272727272 9.398330909090907 0.119357812324 0.47495115388899994 88.85324643999999 9.457671775136468 9.430760240156756 0.10102581818181816 5.922227272727276e-2 2.786909090909262e-2 5.3204925619834725e-2 9.469222314049584e-2 4.5603966942148494e-2 1.020621593930578e-2 3.5072775869834753e-3 7.766862280992689e-4 4.033517999304282e-3 1.2079629896032291e-2 3.5149464811419576e-3 6.350998346169114e-2 0.10990736961656526 5.928698407864881e-2
user_003 Sitting 1.446047538716e12 0.422122 -0.650847 9.4262 0.46044236363636365 -0.6508465454545453 9.457552727272729 0.178186982884 0.42360181740899994 88.85324643999999 9.458067204259704 9.49159945306404 3.832036363636365e-2 4.5454545460899e-7 3.13527272727292e-2 5.098814049586778e-2 5.57385867768595e-2 6.143867768595047e-2 1.4684502692231417e-3 2.0661157030569337e-13 9.829935074381372e-4 4.204520725820437e-3 5.136800779629599e-3 5.889135064763309e-3 6.484227576065199e-2 7.167147814597938e-2 7.674070018421325e-2
user_003 Sitting 1.446047538942e12 0.307161 -0.574206 9.57948 0.41863845454545456 -0.6334282727272728 9.478454545454547 9.434787992100001e-2 0.329712530436 91.7664370704 9.601588279069095 9.509319384860925 0.11147745454545455 5.922227272727276e-2 0.10102545454545364 5.573864462809917e-2 5.478864462809916e-2 4.40204958677692e-2 1.2427222871933884e-2 3.5072775869834753e-3 1.0206142466115519e-2 4.095318860127722e-3 5.583616413305784e-3 2.7879063897821754e-3 6.39946783735001e-2 7.472360010937498e-2 5.280062868737621e-2
user_003 Sitting 1.446047539177e12 0.345482 -0.689167 9.46452 0.3907692727272727 -0.6821995454545454 9.468003636363637 0.119357812324 0.47495115388899994 89.5771388304 9.495864773500779 9.501078447172395 4.5287272727272676e-2 6.9674545454545544e-3 3.4836363636365775e-3 6.84062644628099e-2 5.985547107438018e-2 6.302214876033106e-2 2.0509370710743756e-3 4.854542284297533e-5 1.2135722314051077e-5 7.985403573391432e-3 5.155520228918111e-3 8.248981431104595e-3 8.936108534139137e-2 7.180195142834289e-2 9.082390341261817e-2
user_003 Sitting 1.446047539242e12 0.307161 -0.574206 9.46452 0.42560581818181814 -0.6403957272727273 9.471487272727272 9.434787992100001e-2 0.329712530436 89.5771388304 9.48689618583217 9.503357170811485 0.11844481818181812 6.618972727272732e-2 6.967272727271379e-3 6.492270247933883e-2 6.048900000000001e-2 7.347305785123949e-2 1.4029174954123954e-2 4.381079996438023e-3 4.8542889256179554e-5 7.264995577235909e-3 6.130837874947407e-3 9.196671019083402e-3 8.52349434048965e-2 7.829966714455054e-2 9.589927538351581e-2
user_003 Sitting 1.44604753925e12 0.575403 -0.650847 9.54116 0.4290894545454545 -0.6473630909090909 9.46452 0.331088612409 0.42360181740899994 91.0337341456 9.580627566888193 9.497061195942141 0.1463135454545455 3.48390909090901e-3 7.663999999999938e-2 6.618947107438017e-2 5.1938214876033055e-2 6.428892561983429e-2 2.1407653583479354e-2 1.2137622553718444e-5 5.8736895999999044e-3 7.6180346566506385e-3 5.266982370293012e-3 6.86109609737033e-3 8.728135343044721e-2 7.257397860316749e-2 8.283173363735864e-2
user_003 Sitting 1.446047539363e12 0.460442 -0.727487 9.34956 0.4569588181818181 -0.6717486363636362 9.4262 0.21200683536400003 0.529237335169 87.41427219360001 9.389116910771374 9.462187082718389 3.483181818181913e-3 5.573836363636375e-2 7.663999999999938e-2 8.455800826446282e-2 7.157352066115702e-2 6.365553719008284e-2 1.2132555578513057e-5 3.106765180859517e-3 5.8736895999999044e-3 9.295014362248687e-3 9.007034869194588e-3 5.014259810668705e-3 9.641065481703091e-2 9.49053995787099e-2 7.081143841688788e-2
user_003 Sitting 1.446047539436e12 0.460442 -0.765807 9.54116 0.42560581818181814 -0.7135525454545454 9.447101818181817 0.21200683536400003 0.586460361249 91.0337341456 9.582911944822044 9.484323546353263 3.483618181818188e-2 5.225445454545463e-2 9.405818181818226e-2 7.030644628099174e-2 6.903965289256202e-2 6.903933884297525e-2 1.2135595636694255e-3 2.7305280198429843e-3 8.846941566942232e-3 6.440833891956421e-3 8.33402396231856e-3 8.223606738993262e-3 8.025480603650115e-2 9.129087556989778e-2 9.068410411419006e-2
user_003 Sitting 1.446047539468e12 0.460442 -0.765807 9.34956 0.44999136363636355 -0.7170361818181819 9.4262 0.21200683536400003 0.586460361249 87.41427219360001 9.392163722498294 9.46466076088866 1.0450636363636467e-2 4.8770818181818165e-2 7.663999999999938e-2 6.840630578512398e-2 3.7686694214876054e-2 7.727338842975164e-2 1.0921580040496085e-4 2.3785927061239654e-3 5.8736895999999044e-3 6.29520499386251e-3 2.36428181288505e-3 9.046629361382369e-3 7.934232788280483e-2 4.862388109648437e-2 9.511377061909788e-2
user_003 Sitting 1.446047539525e12 0.460442 -0.727487 9.38788 0.4290893636363636 -0.6821993636363636 9.429683636363636 0.21200683536400003 0.529237335169 88.13229089439999 9.427276121178004 9.464803624816936 3.1352636363636444e-2 4.528763636363642e-2 4.180363636363715e-2 6.397267768595044e-2 5.510515702479342e-2 6.650578512396699e-2 9.829878069504182e-4 2.050970007404964e-3 1.7475440132232066e-3 8.124416326288506e-3 5.511913618561984e-3 5.330891838317037e-3 9.013554418922928e-2 7.424226302155655e-2 7.301295664686534e-2
user_003 Sitting 1.446047539541e12 0.345482 -0.727487 9.4262 0.41515472727272723 -0.6891666363636363 9.4262 0.119357812324 0.529237335169 88.85324643999999 9.460541294634941 9.461245060511372 6.967272727272722e-2 3.832036363636371e-2 0.0 6.618949586776862e-2 6.143910743801655e-2 6.682247933884321e-2 4.854288925619827e-3 1.468450269223146e-3 0.0 8.582264722732536e-3 5.798763261621339e-3 6.938323421187104e-3 9.264051339847236e-2 7.614961104051247e-2 8.329659909736474e-2
user_003 Sitting 1.446047539613e12 0.460442 -0.574206 9.38788 0.4012200909090909 -0.6856830909090909 9.44361818181818 0.21200683536400003 0.329712530436 88.13229089439999 9.41668786039975 9.477938578653424 5.922190909090913e-2 0.11147709090909086 5.573818181818169e-2 6.4289347107438e-2 8.930847107438017e-2 6.143867768595079e-2 3.5072345163719054e-3 1.2427141797553708e-2 3.1067449123966797e-3 5.837369956217128e-3 1.0567076387593537e-2 6.332640552967716e-3 7.640268291242873e-2 0.10279628586477985 7.957788984993078e-2
user_003 Sitting 1.446047539662e12 0.460442 -0.574206 9.50284 0.42212209090909086 -0.664781090909091 9.468003636363637 0.21200683536400003 0.329712530436 90.30396806560002 9.531300406104092 9.501586987084501 3.8319909090909154e-2 9.0575090909091e-2 3.4836363636364e-2 6.618954545454546e-2 8.550803305785125e-2 3.9586776859504624e-2 1.468415432735542e-3 8.203847093190098e-3 1.213572231404984e-3 8.463145261121711e-3 8.296535774210368e-3 2.9379480474831074e-3 9.199535456272621e-2 9.108532139818341e-2 5.4202841691954745e-2
user_003 Sitting 1.446047539743e12 0.307161 -0.650847 9.38788 0.443024 -0.6647812727272728 9.447101818181817 9.434787992100001e-2 0.42360181740899994 88.13229089439999 9.415425672359694 9.48130930593024 0.13586299999999996 1.3934272727272878e-2 5.9221818181818264e-2 6.36560826446281e-2 4.053709090909094e-2 4.718743801652924e-2 1.845875476899999e-2 1.9416395643802073e-4 3.5072237487603405e-3 5.967562775254698e-3 2.700786287416983e-3 2.8695467035311996e-3 7.725000178158378e-2 5.196908973050214e-2 5.3568150085019736e-2
user_003 Sitting 1.44604753984e12 0.345482 -0.650847 9.57948 0.4465079090909091 -0.6647812727272727 9.44710181818182 0.119357812324 0.42360181740899994 91.7664370704 9.607777927290629 9.481999925423107 0.10102590909090908 1.3934272727272767e-2 0.13237818181818106 8.139092561983471e-2 7.917409917355371e-2 6.04885950413228e-2 1.0206234307644627e-2 1.9416395643801764e-4 1.7523983021487402e-2 7.2760015071224645e-3 1.0569256395184072e-2 5.581329016979755e-3 8.529948128284523e-2 0.10280688885081618 7.47082928260294e-2
user_003 Sitting 1.446047539938e12 0.537083 -0.574206 9.4262 0.4499914545454545 -0.6787157272727273 9.457552727272727 0.28845814888899995 0.329712530436 88.85324643999999 9.458933191397696 9.493288075858345 8.70915454545455e-2 0.10450972727272734 3.135272727272742e-2 5.7638661157024804e-2 7.410695041322317e-2 3.895338842975268e-2 7.584937289661166e-3 1.0922283094619848e-2 9.829935074380258e-4 4.463789009020284e-3 8.106787283856505e-3 2.737157005559762e-3 6.68115933728592e-2 9.003769923680027e-2 5.2317845956802944e-2
user_003 Sitting 1.446047540156e12 0.345482 -0.804128 9.46452 0.453475 -0.6926506363636363 9.443618181818183 0.119357812324 0.646621840384 89.5771388304 9.504899709260902 9.480333405363366 0.107993 0.11147736363636362 2.090181818181769e-2 4.718766942148758e-2 5.4788471074380146e-2 6.333884297520678e-2 1.1662488049e-2 1.2427202603314046e-2 4.368860033057645e-4 5.102591182545451e-3 4.019181194645379e-3 5.653040103380977e-3 7.143242388821376e-2 6.339701250568026e-2 7.518670163919267e-2
user_003 Sitting 1.446047540172e12 0.498763 -0.765807 9.4262 0.45347509090909094 -0.6926505454545454 9.45406909090909 0.24876453016900002 0.586460361249 88.85324643999999 9.470399745069793 9.490850263665708 4.528790909090907e-2 7.315645454545461e-2 2.7869090909090843e-2 5.2888297520661126e-2 6.555606611570247e-2 6.3655537190083e-2 2.0509947098264446e-3 5.351866841661166e-3 7.766862280991699e-4 5.354138748736284e-3 5.332058540061606e-3 5.5967744817431225e-3 7.31719806260312e-2 7.30209458995267e-2 7.481159323088316e-2
user_003 Sitting 1.446047540269e12 0.460442 -0.650847 9.2346 0.45695872727272724 -0.6682647272727272 9.394847272727274 0.21200683536400003 0.42360181740899994 85.27783716 9.26895063169359 9.43032809691739 3.4832727272727793e-3 1.741772727272728e-2 0.16024727272727368 5.383861983471073e-2 7.347342975206611e-2 8.645752066115685e-2 1.2133188892562345e-5 3.0337722334710765e-4 2.567918841652923e-2 5.776714665064613e-3 7.215338384983472e-3 1.0210555456048083e-2 7.600470159841832e-2 8.494314795781631e-2 0.10104729316536927
user_003 Sitting 1.446047540318e12 0.422122 -0.535886 9.65612 0.45695872727272724 -0.668264909090909 9.440134545454546 0.178186982884 0.287173804996 93.24065345439999 9.680186684268026 9.475438618282498 3.483672727272724e-2 0.132378909090909 0.2159854545454536 6.428949586776858e-2 5.7955223140495844e-2 8.962446280991737e-2 1.213597567074378e-3 1.752417557209915e-2 4.66497165752062e-2 6.929614733910591e-3 4.520045244982718e-3 1.2364094543050317e-2 8.324430751655389e-2 6.723128174430945e-2 0.11119395011892651
user_003 Sitting 1.446047540325e12 0.383802 -0.574206 9.46452 0.4360567272727273 -0.6508466363636364 9.44361818181818 0.147303975204 0.329712530436 89.5771388304 9.489686788089479 9.476578365490541 5.225472727272734e-2 7.664063636363638e-2 2.0901818181819465e-2 5.6688727272727266e-2 5.763855371900824e-2 8.930776859504132e-2 2.7305565223471146e-3 5.873787142223143e-3 4.368860033058388e-4 5.49977403832757e-3 4.470402678275729e-3 1.2349752325770074e-2 7.41604614220244e-2 6.686106997555251e-2 0.11112943950983499
user_003 Sitting 1.44604754035e12 0.345482 -0.804128 9.34956 0.44650772727272714 -0.6647813636363636 9.408781818181819 0.119357812324 0.646621840384 87.41427219360001 9.390434060591023 9.44335301311665 0.10102572727272713 0.1393466363636363 5.9221818181818264e-2 5.478842148760328e-2 6.143899999999997e-2 8.645752066115717e-2 1.0206197570983441e-2 1.941748506585949e-2 3.5072237487603405e-3 4.3148482379737e-3 5.454524538504126e-3 1.1695526568294479e-2 6.568750442796331e-2 7.38547529851947e-2 0.10814585784159501
user_003 Sitting 1.446047540366e12 0.460442 -0.727487 9.38788 0.44999136363636355 -0.668265 9.412265454545453 0.21200683536400003 0.529237335169 88.13229089439999 9.427276121178004 9.447275769909304 1.0450636363636467e-2 5.9222e-2 2.4385454545454266e-2 6.017223966942149e-2 6.365590909090904e-2 6.238876033057863e-2 1.0921580040496085e-4 3.5072452839999997e-3 5.946503933884161e-4 5.200762819015026e-3 5.541682701910585e-3 7.90476821637862e-3 7.21163145135345e-2 7.444247914941163e-2 8.890876343971171e-2
user_003 Sitting 1.44604754048e12 0.498763 -0.765807 9.4262 0.4987627272727273 -0.7414219090909092 9.44361818181818 0.24876453016900002 0.586460361249 88.85324643999999 9.470399745069793 9.486452058500035 2.7272727270988284e-7 2.43850909090908e-2 1.741818181818111e-2 6.397279338842976e-2 7.505698347107435e-2 5.6371570247933954e-2 7.43801652797708e-14 5.946326586446228e-4 3.0339305785121503e-4 5.997347520679191e-3 8.279990516842221e-3 4.944755219233634e-3 7.744254335104957e-2 9.099445322019481e-2 7.031895348505718e-2
user_003 Sitting 1.446047540593e12 0.460442 -0.689167 9.50284 0.42560572727272733 -0.668265 9.502839999999999 0.21200683536400003 0.47495115388899994 90.30396806560002 9.538916398357468 9.536132101776438 3.483627272727269e-2 2.0901999999999976e-2 1.7763568394002505e-15 5.858879338842975e-2 4.3703983471074376e-2 9.310809917355378e-2 1.213565897528923e-3 4.36893603999999e-4 3.1554436208840472e-30 4.6480467704342575e-3 2.341116897894815e-3 1.3706746729977431e-2 6.817658520661077e-2 4.838508962371378e-2 0.11707581616191036
user_003 Sitting 1.446047540649e12 0.652044 -0.574206 9.50284 0.4465077272727273 -0.6299446363636364 9.443618181818183 0.42516137793599995 0.329712530436 90.30396806560002 9.542475673218771 9.476115480764772 0.20553627272727265 5.573863636363641e-2 5.9221818181818264e-2 8.360787603305782e-2 6.93565371900826e-2 7.442314049586765e-2 4.2245159406619805e-2 3.1067955836776907e-3 3.5072237487603405e-3 1.1187097730272723e-2 6.7685262636356025e-3 8.376958139143426e-3 0.10576907738215703 8.227105361933565e-2 9.152572392034616e-2
user_003 Sitting 1.44604754073e12 0.422122 -0.765807 9.38788 0.47437709090909086 -0.6856831818181817 9.450585454545454 0.178186982884 0.586460361249 88.13229089439999 9.428517287385805 9.487907311509131 5.225509090909086e-2 8.012381818181835e-2 6.270545454545484e-2 7.125673553719011e-2 5.415497520661158e-2 7.378975206611554e-2 2.7305945259173503e-3 6.419826240033085e-3 3.931974029752103e-3 7.686441355646129e-3 3.738938656548464e-3 6.12853976859502e-3 8.767235228762901e-2 6.114686137937469e-2 7.828499069805794e-2
user_003 Sitting 1.446047540852e12 0.537083 -0.650847 9.31124 0.44650772727272725 -0.6856831818181818 9.401814545454545 0.28845814888899995 0.42360181740899994 86.6991903376 9.349398392618532 9.437789541464076 9.057527272727273e-2 3.483618181818182e-2 9.057454545454569e-2 4.782117355371898e-2 5.732195867768597e-2 9.72251239669423e-2 8.203880029619835e-3 1.2135595636694218e-3 8.203748284297563e-3 3.1343671547543177e-3 4.990056541979716e-3 1.4301397123365868e-2 5.598541912636109e-2 7.064033226124942e-2 0.11958844895459539
user_003 Sitting 1.446047540925e12 0.460442 -0.574206 9.46452 0.4848280000000001 -0.6891670000000001 9.454069090909089 0.21200683536400003 0.329712530436 89.5771388304 9.49309529058884 9.492107685859132 2.4386000000000074e-2 0.11496100000000009 1.0450909090911509e-2 4.433761157024795e-2 7.062329752066118e-2 5.478809917355385e-2 5.946769960000036e-4 1.321603152100002e-2 1.0922150082649681e-4 2.8111200184079647e-3 6.5169880832118745e-3 3.972794186626621e-3 5.301999640143296e-2 8.072786435433477e-2 6.303010539913939e-2
user_003 Sitting 1.446047540933e12 0.345482 -0.765807 9.4262 0.4813443636363636 -0.699617909090909 9.450585454545454 0.119357812324 0.586460361249 88.85324643999999 9.463565111181566 9.48928338586127 0.13586236363636361 6.618909090909098e-2 2.4385454545454266e-2 5.098821487603307e-2 7.34735619834711e-2 5.1304462809917434e-2 1.8458581852859497e-2 4.3809957553719095e-3 5.946503933884161e-4 4.1317176202314045e-3 6.804936827912101e-3 3.6694011287753763e-3 6.427843822178168e-2 8.2492040997372e-2 6.057558195160304e-2
user_003 Sitting 1.446047541118e12 0.383802 -0.612526 9.38788 0.443024 -0.6717485454545453 9.46452 0.147303975204 0.375188100676 88.13229089439999 9.415666889301043 9.499323719754216 5.9222e-2 5.9222545454545306e-2 7.664000000000115e-2 5.76385950413223e-2 7.030660330578511e-2 7.632330578512493e-2 3.5072452839999997e-3 3.507309890115685e-3 5.873689600000177e-3 5.4037578223200605e-3 7.833167167294515e-3 9.64789923966959e-3 7.351025657906562e-2 8.850518158443897e-2 9.822372035139776e-2
user_003 Sitting 1.446047541158e12 0.537083 -0.574206 9.50284 0.4674095454545455 -0.6299444545454546 9.474970909090908 0.28845814888899995 0.329712530436 90.30396806560002 9.535310102189914 9.508476174425821 6.967345454545448e-2 5.573845454545456e-2 2.786909090909262e-2 5.9855570247933865e-2 0.10007608264462808 9.089123966942222e-2 4.854390268297512e-3 3.1067753151157044e-3 7.766862280992689e-4 6.554502585894815e-3 1.4869794603729529e-2 1.105123003816697e-2 8.095988257090554e-2 0.12194176726507423 0.10512483074025361
user_003 Sitting 1.446047541175e12 0.460442 -0.689167 9.46452 0.4778605454545454 -0.6194935454545454 9.450585454545454 0.21200683536400003 0.47495115388899994 89.5771388304 9.500741908906535 9.483763584885539 1.741854545454541e-2 6.96734545454546e-2 1.393454545454631e-2 6.523947933884294e-2 8.677486776859503e-2 6.04885950413228e-2 3.0340572575206454e-4 4.8543902682975275e-3 1.9417155702481723e-4 6.91085958427573e-3 1.1449687157067612e-2 5.543818602554532e-3 8.313157994574463e-2 0.10700321096615564 7.445682374742112e-2
user_003 Sitting 1.446047541192e12 0.498763 -0.689167 9.34956 0.4848280000000001 -0.6229772727272729 9.443618181818183 0.24876453016900002 0.47495115388899994 87.41427219360001 9.388183417342145 9.477402355192464 1.393499999999992e-2 6.61897272727271e-2 9.405818181818226e-2 6.492290082644625e-2 8.772497520661153e-2 5.352132231405013e-2 1.9418422499999777e-4 4.381079996437993e-3 8.846941566942232e-3 6.8987275471728005e-3 1.1558912227920352e-2 3.8139265490609105e-3 8.305857901007457e-2 0.10751238174238516 6.1756995952368915e-2
user_003 Sitting 1.4460475412e12 0.498763 -0.689167 9.27292 0.4778607272727273 -0.6369119090909092 9.422716363636365 0.24876453016900002 0.47495115388899994 85.98704532639998 9.311861307518384 9.457088391091498 2.0902272727272686e-2 5.225509090909075e-2 0.14979636363636573 5.5422041322314014e-2 7.91742066115702e-2 6.397223140495938e-2 4.3690500516528754e-4 2.730594525917339e-3 2.243895055867831e-2 5.508618789685197e-3 9.86099776814199e-3 5.74350639699485e-3 7.422006999245688e-2 9.930255670496098e-2 7.578592479474569e-2
user_003 Sitting 1.44604754196e12 0.460442 -0.650847 9.46452 0.44999127272727274 -0.678716 9.44361818181818 0.21200683536400003 0.42360181740899994 89.5771388304 9.49803913885245 9.478809400678257 1.0450727272727278e-2 2.7869000000000033e-2 2.0901818181819465e-2 4.528754545454547e-2 4.148710743801653e-2 1.9951735537190984e-2 1.0921770052892573e-4 7.766811610000018e-4 4.368860033058388e-4 2.986535792406464e-3 2.2738162678332076e-3 5.769984336589671e-4 5.4649206695124714e-2 4.7684549571461905e-2 2.40207916950913e-2
user_003 Sitting 1.446047542413e12 0.422122 -0.765807 9.4262 0.4534750000000001 -0.6821996363636363 9.450585454545454 0.178186982884 0.586460361249 88.85324643999999 9.46667279376091 9.486175161191744 3.1353000000000075e-2 8.360736363636367e-2 2.4385454545454266e-2 2.7235900826446276e-2 2.8819264462809923e-2 2.4702148760330802e-2 9.830106090000048e-4 6.990191254223147e-3 5.946503933884161e-4 1.3062715649120968e-3 1.4066530360563494e-3 1.1032474830954288e-3 3.6142379071003296e-2 3.750537342910146e-2 3.3215169472628446e-2
user_003 Standing 1.446047630907e12 -0.689167 -9.54116 0.344284 -0.8041277272727272 -9.4262 0.4035064545454546 0.47495115388899994 91.0337341456 0.11853147265599999 9.572210652307282 9.470404311628682 0.11496072727272721 0.11495999999999995 5.9222454545454606e-2 8.497683663911854e-2 6.639318273645532e-2 8.869623911845728e-2 1.3215968815074367e-2 1.3215801599999988e-2 3.507299122388437e-3 1.0229017505059551e-2 6.166949670227525e-3 1.3160927524382622e-2 0.10113860541385546 7.852992850007903e-2 0.11472108578802165
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user_004 Jogging 1.446046324307e12 3.14286 -4.06135 -2.41478 7.974707272727273 -5.670806363636364 -0.7391351818181818 9.877568979600001 16.4945638225 5.8311624484 5.674794732014543 13.21582437877326 4.831847272727273 1.6094563636363644 1.675644818181818 7.802776611570248 4.166146611570248 3.0396570413223145 23.34674806696199 2.5903497864495892 2.807785556699578 88.72848169522757 33.40847150884982 14.554685959196457 9.419579698438119 5.7800061858833525 3.8150604135709902
user_004 Jogging 1.446046326574e12 12.0715 -5.70913 -2.29982 7.38248581818182 -5.493142727272727 -0.5266323636363637 145.72111225 32.5941653569 5.2891720324 13.55007194221861 13.15136563242288 4.68901418181818 0.21598727272727292 1.7731876363636363 7.681798446280993 5.468403966942149 2.036678347107438 21.98685399729202 4.665050198016537e-2 3.1441943937528594 85.34078258003082 48.436344589920964 6.831764064025652 9.238007500539865 6.959622445932032 2.6137643474547687
user_004 Jogging 1.446046326639e12 1.61005 0.268841 -2.8363 7.281459454545455 -6.033110818181819 -0.7112664545454546 2.5922610025 7.2275483281e-2 8.04459769 3.2724813484237 12.828283111638287 5.671409454545455 6.301951818181819 2.1250335454545457 7.794859388429751 5.05574826446281 2.2292298016528926 32.16488520110758 39.71459671868514 4.515767569307116 86.48260182948019 41.36213986419955 7.242283975172693 9.299602240390724 6.431340440701265 2.6911491922917787
user_004 Jogging 1.44604632761e12 15.32872 -14.67608 -5.0972 5.4734360909090904 -3.793111272727273 -0.34896536363636366 234.96965683840003 215.38732416640002 25.98144784 21.8251787815083 11.905846007617221 9.855283909090911 10.882968727272727 4.7482346363636365 7.350532371900827 5.82310479338843 3.0558068677685952 97.12662092878622 118.43900831879617 22.545732161963315 77.70046956614337 45.631422866974525 15.833528605261323 8.814786983594292 6.755103468265643 3.9791366658185194
user_004 Jogging 1.446046327869e12 -10.65245 4.13919 5.36425 6.501117909090909 -4.55603409090909 -0.4186382727272728 113.4746910025 17.1328938561 28.7751780625 12.624688626698878 12.71974471624847 17.15356790909091 8.69522409090909 5.782888272727273 7.723284404958677 6.096096619834711 2.903793123966942 294.24489201179347 75.6069219911258 33.441796774846615 82.30222588031641 49.14387324683357 13.352193441058244 9.072057422675211 7.010269127988852 3.654065330704727
user_004 Jogging 1.446046327934e12 12.22478 -6.05401 -4.63736 6.678785181818182 -3.8593013636363636 -1.0247964545454544 149.4452460484 36.6510370801 21.505107769600002 14.408379190530072 12.460816653562178 5.545994818181819 2.1947086363636363 3.6125635454545457 7.868965173553719 5.46998726446281 2.945280504132232 30.75805852329958 4.816745998529132 13.050615369947117 83.68466370606724 42.08347585402162 13.633011805578548 9.147932209306497 6.487177803484472 3.6922908614542473
user_004 Jogging 1.446046328581e12 -3.17999 -1.68549 -0.1922 8.981486363636364 -6.047043636363636 -1.160660090909091 10.1123364001 2.8408765400999996 3.694084e-2 3.604185591808502 13.103828097993564 12.161476363636364 4.361553636363636 0.9684600909090909 6.4954519752066116 5.569113595041323 2.5399093471074377 147.90150734328597 19.02315012287686 0.9379149476836446 54.42817332005659 46.03700027441227 9.23046015355527 7.377545209624715 6.785057131256322 3.0381672359426286
user_004 Jogging 1.446046328905e12 19.50564 -5.67081 1.03405 7.100308181818182 -4.7511209090909094 -0.4221227272727273 380.4699918096 32.158086056100004 1.0692594024999997 20.33955105866892 13.054363658769024 12.405331818181818 0.9196890909090909 1.4561727272727272 8.1137726446281 5.9206470247933884 2.656452793388429 153.8922575191942 0.84582802393719 2.120439011652892 88.05080876348978 50.20722639022751 9.792734096364068 9.383539245055129 7.08570577925922 3.1293344494259587
user_004 Jogging 1.446046329617e12 3.56439 -7.05034 6.05401 5.84618909090909 -4.925304545454545 -0.7042981818181816 12.7048760721 49.70729411560001 36.6510370801 9.953050148964387 12.291283422299722 2.28179909090909 2.125035454545455 6.758308181818181 6.877387685950413 6.140116363636364 2.6222487603305784 5.20660709127355 4.5157756830752085 45.67472948043057 77.1762593682838 52.764850806034104 10.386124266575505 8.785001956077403 7.263941822869598 3.2227510401170467
user_004 Jogging 1.446046329683e12 19.00747 -7.08866 -0.1922 6.992314545454545 -4.904402727272727 -0.44650727272727264 361.28391580090005 50.2491005956 3.694084e-2 20.28718702128267 13.209857240769736 12.015155454545457 2.1842572727272733 0.2543072727272726 7.863264297520661 6.199972148760332 2.42336347107438 144.36396059689346 4.770979833461986 6.467218896198342e-2 90.17570123666434 52.98692180883688 9.849859374475129 9.496088733613663 7.279211620006447 3.1384485617061064
user_004 Jogging 1.446046330071e12 17.05314 -7.05034 -3.75599 7.476543181818181 -5.385147272727274 -0.7356514545454546 290.80958385959997 49.70729411560001 14.107460880100001 18.831472031025616 13.604796241409561 9.576596818181818 1.6651927272727267 3.0203385454545457 6.897656694214876 5.849389256198347 2.7007900661157027 91.71120661801011 2.7728668189619814 9.122444929158481 82.83997545198206 51.75787714759219 10.641467584078235 9.101646853838158 7.194294763741071 3.2621262366864707
user_004 Jogging 1.446046330265e12 4.02423 -3.52487 -1.76333 6.295580454545455 -4.420171818181819 -1.073566909090909 16.1944270929 12.424708516899999 3.1093326889000004 5.632802881221745 11.50053752911144 2.2713504545454546 0.8953018181818191 0.689763090909091 6.918558636363636 5.134288347107438 2.0914646446280996 5.159032887363844 0.8015653456396711 0.475773121580463 77.31788883703922 43.5971327069604 7.869029743331553 8.793059128485332 6.602812484612932 2.8051790929157363
user_004 Jogging 1.446046330329e12 12.18646 -8.04667 4.48288 7.079405 -4.510747272727273 -1.2163969090909088 148.5098073316 64.7488980889 20.0962130944 15.27595884109734 11.984438319305344 5.107055 3.5359227272727276 5.699276909090909 7.175400082644629 5.26255082644628 1.995189074380165 26.082010773024997 12.502749533243804 32.48175728649682 79.21565280810752 44.32322123879391 6.669668634792123 8.900317567823494 6.657568718293031 2.582570160671753
user_004 Jogging 1.446046330848e12 16.01849 -5.05768 -2.33814 8.019996363636364 -5.001943636363635 -0.8088077272727273 256.5920218801 25.580126982400003 5.466898659600001 16.95992474989497 12.894361379172755 7.998493636363635 5.573636363636503e-2 1.529332272727273 6.6452502066115695 5.332539917355372 2.045543966942149 63.975900450949574 3.1065422314051137e-3 2.3388572004051658 71.60663987257274 44.567811622551844 7.075141314559144 8.462070661048202 6.675912793210517 2.6599137795348073
user_004 Jogging 1.446046331495e12 8.62267 -8.96635 -0.460442 6.427961 -3.901106363636364 -0.32458081818181816 74.35043792889999 80.3954323225 0.21200683536400003 12.448207786133874 11.40122148597141 2.1947089999999996 5.065243636363636 0.13586118181818185 6.147401809917355 4.683945950413223 1.9023978347107438 4.816747594680998 25.65669309572231 1.845826072503307e-2 68.3485394276605 34.49821943043216 5.597293831373793 8.267317547285849 5.873518488132318 2.3658600616633674
user_004 Jogging 1.446046331561e12 18.04947 -17.93331 -5.40376 6.612595545454545 -5.071618181818182 -0.6032735454545455 325.7833672809 321.6036075561 29.2006221376 26.01129748733423 12.22407355301134 11.436874454545453 12.861691818181818 4.800486454545455 6.45998188429752 5.848123719008263 2.1997754876033055 130.80209728903435 165.42311642588513 23.044670200274393 74.42364823112277 49.53640214712795 7.47964046772554 8.626914177799774 7.038210152242398 2.734893136436146
user_004 Jogging 1.44604633318e12 18.31771 -7.47186 -3.10454 7.713432727272728 -4.754604545454545 -0.7042989090909091 335.53849964410006 55.828691859600006 9.638168611600001 20.02511822974586 13.149988530002542 10.604277272727273 2.7172554545454553 2.400241090909091 6.938827685950414 5.259700247933884 3.0469385041322314 112.45069647688018 7.383477205257028 5.761157294488465 77.11467456979197 48.156471003066486 12.895906901866105 8.78149614643154 6.939486364498923 3.5910871476289885
user_004 Jogging 1.446046334476e12 6.13185 -5.93905 -3.0279 7.685562727272727 -6.245612363636363 -0.4499909090909093 37.5995844225 35.2723149025 9.16817841 9.05759779052923 13.862535036033972 1.5537127272727274 0.30656236363636324 2.5779090909090905 7.415138760330578 5.167541801652892 3.0304713057851242 2.4140232388892566 9.398048279831381e-2 6.645615280991733 82.42637556285928 43.10521410638309 12.187500293637099 9.078897265794964 6.565456123254735 3.4910600529978137
user_004 Jumping 1.446046440753e12 19.54396 -19.58108 -6.89825 4.1884622857142855 -2.999333571428571 -1.5224617142857144 381.96637248159993 383.4186939664 47.5858530625 28.512644905559004 7.446083883383553 15.355497714285713 16.581746428571428 5.375788285714286 3.529288130612245 4.4176791921768706 1.270878112244898 235.79131005343376 274.9543146214413 28.899099692822944 40.42154505965868 49.56948207287595 4.601596063441028 6.357794040361695 7.040559784056659 2.14513311089103
user_004 Jumping 1.446046441725e12 0.767005 1.30349 1.03405 3.717668909090908 -2.779365454545454 -0.7217180909090909 0.5882966700250001 1.6990861801000001 1.0692594024999997 1.832114148361122 6.847435681349492 2.950663909090908 4.082855454545454 1.7557680909090907 4.667160314049585 5.080450140495867 1.426719958677686 8.706417504411638 16.669708662711567 3.082721589054553 34.66505868718512 41.30950542823461 3.54571694005584 5.887704025100542 6.427247111184897 1.8830074190124264
user_004 Jumping 1.446046441855e12 19.50564 -19.58108 -6.28513 6.448862999999999 -5.51752609090909 -1.188529 380.4699918096 383.4186939664 39.5028591169 28.34416244825202 10.589792564338788 13.056777 14.06355390909091 5.096601 6.050492041322314 6.463148644628098 1.8384256611570249 170.479425627729 197.7835485539062 25.975341753200997 53.52722088151006 62.083623176617515 5.872172381026635 7.31622996368417 7.879316161737484 2.423256565249878
user_004 Jumping 1.446046442243e12 0.575403 1.15021 -0.690364 3.6410292727272737 -2.856005545454545 -0.826227181818182 0.331088612409 1.3229830441 0.476602452496 1.4596828796026209 7.0567311800225605 3.0656262727272736 4.006215545454545 0.13586318181818202 4.304226818181818 5.246715801652892 1.354511909090909 9.398064444035716 16.049762996641654 1.8458804173760387e-2 29.45904764510686 39.08100872537067 3.7849847946391066 5.427618966462814 6.251480522673862 1.9455037380172537
user_004 Jumping 1.446046442372e12 18.16443 -17.3585 1.60885 5.591881181818182 -4.914851 -0.906352 329.9465172249 301.31752224999997 2.5883983225 25.17642623164376 9.544468036485442 12.572548818181819 12.443649 2.515202 5.156457942148761 5.815502611570248 1.753866247933884 158.06898378556505 154.844400435201 6.326241100803999 42.76716719331571 50.12337746871192 4.928973948004476 6.539661091625139 7.079786541182716 2.2201292638052577
user_004 Jumping 1.446046443215e12 -0.880768 -0.612526 -1.91661 4.623420909090909 -3.2287572727272735 -1.8016529999999997 0.775752269824 0.375188100676 3.6733938920999996 2.196436719461774 8.431501330251807 5.504188909090908 2.6162312727272736 0.1149570000000002 6.099263876033058 4.956621512396695 1.8605928016528923 30.296095546959364 6.84466607239617 1.3215111849000045e-2 44.71450237822124 32.7193750866241 6.223155739080556 6.686890336936986 5.720085234209723 2.494625370487632
user_004 Jumping 1.446046444575e12 0.881966 -4.94272 3.79311 6.790261454545456 -6.263030272727272 -0.8749987272727274 0.7778640251560001 24.430480998399997 14.387683472099999 6.292537524374089 11.126299442112229 5.908295454545455 1.3203102727272729 4.6681087272727275 6.9910826033057845 6.443830049586777 1.6366885537190081 34.90795517820249 1.7432192162691655 21.791239089639802 58.32619930429955 59.72487196810328 3.9530999690558626 7.637159112150247 7.728186848679532 1.9882404203354942
user_004 Jumping 1.446046444769e12 0.268841 -1.95374 3.7722e-2 3.2195042727272734 -2.3926784545454542 -0.9725412727272725 7.2275483281e-2 3.8170999876000002 1.422949284e-3 1.9725106894932154 7.197199404975865 2.9506632727272732 0.4389384545454542 1.0102632727272725 5.604267272727273 5.468403644628099 1.521094487603306 8.706413749021623 0.1926669668787518 1.0206318802216192 38.98070197949247 47.5268644494463 3.7172557078802515 6.243452729018814 6.893973052561658 1.9280185963522891
user_004 Jumping 1.446046445287e12 0.11556 -1.11069 0.382604 3.397171909090909 -2.2568159090909092 -0.9237708181818182 1.3354113599999998e-2 1.2336322760999998 0.146385820816 1.180411881724341 7.075012334908058 3.281611909090909 1.1461259090909093 1.3063748181818182 4.881565661157025 5.449401462809918 1.427669438016529 10.768976721887281 1.3136045994894632 1.7066151655795787 33.09439934785701 45.55143298526623 2.6018435675532396 5.752773187590226 6.749180171344237 1.6130231143890157
user_004 Jumping 1.446046445611e12 2.8363 -10.65245 1.80046 6.518536181818181 -6.203808181818181 -0.9899598181818184 8.04459769 113.4746910025 3.2416562115999996 11.16964390229608 10.831203183334122 3.682236181818181 4.448641818181819 2.7904198181818183 6.020091264462809 6.157535694214877 1.3522956859504134 13.558863298690936 19.790414026476036 7.786442761701852 50.54368804665865 54.31564822609823 2.9312400316853586 7.1094084174886625 7.36991507590815 1.712086455669035
user_004 Jumping 1.44604644574e12 -0.765807 2.33814 -1.64837 3.5330348181818176 -2.9117445454545456 -0.8122925454545453 0.586460361249 5.466898659600001 2.7171236568999997 2.9615000722183007 7.545395689822752 4.298841818181818 5.249884545454545 0.8360774545454546 4.913235181818182 5.647971008264463 1.154676611570248 18.480040977748757 27.561287740602477 0.6990255099992067 32.672137932914204 43.8247559736747 2.23586034254455 5.715954682545533 6.620026886174609 1.495279352677803
user_004 Jumping 1.446046446387e12 1.30349 -1.57053 -1.76333 3.4668452727272734 -2.702724454545454 -0.9307371818181818 1.6990861801000001 2.4665644809 3.1093326889000004 2.6972177053215414 6.522886174272082 2.1633552727272733 1.132194454545454 0.8325928181818183 4.403352826446281 4.768820842975207 1.425451826446281 4.680106036036896 1.2818642829034783 0.6932108008879423 29.814088068487422 33.13063707336669 2.8420858909499063 5.460227840345806 5.755921913418101 1.6858487153211306
user_004 Jumping 1.446046446712e12 -2.10702 -2.18366 4.06135 5.431632909090909 -5.245799181818182 -0.7913907272727272 4.439533280399999 4.768370995600001 16.4945638225 5.069760161832116 9.726178424784191 7.538652909090908 3.0621391818181816 4.852740727272727 6.073928570247935 6.269962735537191 2.0376260165289257 56.83128768374481 9.376696368826122 23.54909256613144 51.70801020713879 53.619849938282414 5.52751424180559 7.190828200363209 7.322557609079113 2.3510666179003925
user_004 Jumping 1.446046447294e12 0.690364 6.13185 -2.41478 4.372599 -3.9324581818181814 -0.8088090909090909 0.476602452496 37.5995844225 5.8311624484 6.626262092869252 9.25374689473398 3.6822350000000004 10.06430818181818 1.605970909090909 5.767999487603306 6.21485658677686 1.7959872231404963 13.558854595225002 101.29029917861237 2.5791425608462806 46.44946369914939 57.41211919025554 6.140247961835453 6.815384339796942 7.577078539269309 2.477952372794008
user_004 Jumping 1.446046447618e12 14.94552 -13.18159 -4.67568 4.574651181818182 -3.8662689090909086 -1.0700833636363638 223.36856807040002 173.7543149281 21.861983462399998 20.469119826238256 8.255475939941938 10.370868818181819 9.31532109090909 3.605596636363636 5.605217123966942 5.653037636363636 1.7095292644628097 107.55492004393595 86.77520702673573 13.000327104156765 46.00866792942744 48.25952326499671 5.097796350635938 6.782968961260802 6.946907460517717 2.2578300092424888
user_004 Jumping 1.446046449302e12 19.08411 -17.97163 -4.06255 6.292096636363637 -5.238832727272728 -1.3313578181818182 364.2032544921 322.97948485690006 16.5043125025 26.527100328748713 10.742951779313438 12.79201336363636 12.732797272727273 2.731192181818182 6.28168061983471 6.237977859504133 1.770017694214876 163.63560589545125 162.12412638837108 7.459410734024761 57.181595329683844 55.44002913204303 5.704743067659652 7.561851316290466 7.445806143866696 2.3884603969209226
user_004 Jumping 1.446046449884e12 9.77228 -10.88237 0.305964 6.661365181818183 -5.935567 -1.4289012727272727 95.4974563984 118.4259768169 9.361396929600001e-2 14.629321487498865 10.730218018448529 3.1109148181818176 4.946803 1.7348652727272729 5.927614074380164 5.7094111404958685 1.4390697024793389 9.677791005983211 24.470859920809 3.009757514515075 50.27496977080698 50.6429279554915 3.326290719735871 7.090484452476218 7.1163844721523795 1.823812139376167
user_004 Jumping 1.446046449949e12 -2.0687 0.575403 1.07237 5.222611545454545 -4.946205818181818 -1.0387312727272728 4.279519690000001 0.331088612409 1.1499774169 2.4001220217541026 9.358456768935065 7.291311545454546 5.521608818181818 2.111101272727273 5.7591315454545455 5.5906493801652895 1.4365365619834711 53.16322405287876 30.488163941023213 4.456748583710711 47.505802565996596 49.17626807639828 3.315524702677734 6.892445325571803 7.012579274161418 1.8208582324491203
user_004 Jumping 1.446046450273e12 1.03525 0.996927 -2.33814 3.6863156363636365 -2.5598956363636356 -1.1815612727272728 1.0717425625 0.993863443329 5.466898659600001 2.7445408842698993 6.7919229626582425 2.6510656363636365 3.5568226363636355 1.1565787272727273 4.373900206611571 4.6421415041322325 1.104005090909091 7.028149008308133 12.650987266548762 1.3376743523798016 30.204080410236873 35.12321301457914 2.2349330079064456 5.495823906407198 5.926484034786489 1.4949692330969375
user_004 Jumping 1.446046450403e12 15.2904 -16.4005 -3.44943 6.609110272727273 -5.649906818181818 -1.5125094545454545 233.79633216 268.97640025000004 11.8985673249 22.686368147742378 10.73749835209278 8.681289727272727 10.750593181818182 1.9369205454545455 5.950099537190083 6.178438495867769 1.3551456363636365 75.36479132885098 115.57525376095559 3.751661199403934 52.523254056726394 57.86450307308849 2.7311461983206873 7.247292877807989 7.6068720952234035 1.6526179831772034
user_004 Jumping 1.446046450531e12 0.422122 6.63001 -3.37279 3.9545577272727273 -3.2078577272727276 -0.9237703636363637 0.178186982884 43.95703260010001 11.375712384100002 7.450565882339677 8.460411268528444 3.5324357272727274 9.837867727272728 2.4490196363636363 5.265083752066116 5.7806671487603305 1.6712091074380166 12.478102167312802 96.78364141931428 5.997697179294677 39.42287793470751 48.03158359135643 4.1075853388824015 6.278764045153115 6.9304822048221455 2.0267178735291207
user_004 Jumping 1.446046450921e12 19.54396 -18.12491 -4.9056 6.191071363636365 -5.245801363636364 -1.7528832727272723 381.96637248159993 328.5123625081 24.064911359999996 27.10246568763993 10.580288705372087 13.352888636363634 12.879108636363636 3.1527167272727272 6.375738710743801 6.294983049586777 1.9831561570247935 178.29963493512906 165.8714392672564 9.939622762425255 57.35020530279037 57.54984604088095 5.6981677095347285 7.572991833006977 7.586161482652538 2.3870835154084427
user_004 Jumping 1.446046451179e12 -0.689167 -1.41725 -0.537083 3.553937181818182 -2.316038363636364 -1.435869 0.47495115388899994 2.0085975625 0.28845814888899995 1.6649344927888305 7.489633255365871 4.2431041818181825 0.898788363636364 0.8987860000000001 4.800173760330579 5.271100983471074 1.8400087190082643 18.003933097762946 0.807820522608133 0.8078162737960002 35.33453433582328 40.798926170358726 4.5008850493141415 5.944285855830226 6.387403711239703 2.1215289414274183
user_004 Jumping 1.446046451309e12 5.99e-4 2.22318 -0.613724 3.790826000000001 -3.0301892727272732 -1.1258224545454545 3.5880100000000004e-7 4.9425293124000005 0.37665714817600005 2.306336232941112 7.049070078541021 3.790227000000001 5.253369272727273 0.5120984545454544 4.614273231404958 4.632957049586778 1.697811479338843 14.36582071152901 27.597888715635076 0.26224482714784286 34.150457092262734 33.510629728040335 4.005569173775021 5.843839242506824 5.788836647206443 2.0013918091605705
user_004 Jumping 1.446046451762e12 0.920286 -2.41358 0.344284 3.5051649090909085 -2.3717772727272726 -1.3940651818181817 0.8469263217960001 5.8253684164 0.11853147265599999 2.6059213746488976 7.351790595676024 2.5848789090909086 4.180272727272749e-2 1.7383491818181818 5.23436547107438 4.90373214876033 1.4349531900826447 6.681598974663006 1.7474680074380348e-3 3.021857877927942 39.88697797769975 39.20278665489539 2.894475197684697 6.315613824300829 6.2612128741079704 1.7013157254562414
user_004 Jumping 1.446046451892e12 -0.382604 0.690364 7.6042e-2 3.557419909090909 -2.660921272727273 -1.2129147272727272 0.146385820816 0.476602452496 5.782385764e-3 0.7929506031752546 6.9635497729535 3.9400239090909093 3.3512852727272726 1.2889567272727271 5.185593942148761 4.676977388429751 1.325692917355372 15.523788404208009 11.23111297919871 1.6614094447816194 39.5295646746675 36.15230679208021 2.620653828008208 6.287254144272164 6.0126788365985595 1.6188433611712432
user_004 Jumping 1.446046452021e12 19.54396 -19.08292 -7.43474 6.243325454545455 -5.102970363636363 -1.8922294545454543 381.96637248159993 364.15783572640004 55.2753588676 28.30900152028679 10.349931781329445 13.300634545454542 13.979949636363639 5.542510545454546 6.506536148760332 5.7812990247933875 1.7316982231404958 176.90687931173878 195.43899183526383 30.719423146474846 58.50151760448834 53.12020252076061 5.359819226407735 7.648628478654741 7.288360756765584 2.315128339079226
user_004 Jumping 1.446046452538e12 18.54763 -15.40417 -3.98591 4.9648212727272725 -3.9847141818181813 -1.3940644545454548 344.01457861690005 237.28845338890002 15.8874785281 24.437481673321006 8.936126658691224 13.58280872727273 11.41945581818182 2.5918455454545453 5.416781504132231 5.744247553719009 1.6895778347107437 184.49269292167622 130.4039711834066 6.717663331492569 45.55107155931277 48.760047922413534 5.977856426539219 6.749153395746222 6.982839531480981 2.444965526656607
user_004 Jumping 1.446046452993e12 0.613724 2.10822 -1.38013 3.665413818181818 -2.7480122727272733 -1.157175909090909 0.37665714817600005 4.444591568400001 1.9047588169000003 2.5934547486848505 7.334066225954348 3.051689818181818 4.856232272727274 0.22295409090909102 4.790039247933884 5.016162074380166 1.2848392396694217 9.312810746394577 23.582991886677906 4.970852665309922e-2 36.88789352842561 41.51884982458453 3.4438666456618092 6.073540444289937 6.443512227394662 1.8557657841607624
user_004 Jumping 1.446046453445e12 3.0279 -1.45557 -7.7239e-2 3.675864363636364 -2.6922731818181824 -1.1362745454545455 9.16817841 2.1186840249000003 5.965863121e-3 3.3604803671530354 7.688581707506595 0.6479643636363641 1.2367031818181824 1.0590355454545455 5.11655426446281 4.447690247933884 1.4346366611570247 0.4198578165426783 1.5294347599192164 1.1215562865362068 38.29682912226736 35.61574287410279 4.090531801082638 6.1884431905179 5.967892666101058 2.0225063166978337
user_004 Jumping 1.44604645351e12 -7.6042e-2 0.11556 -1.26517 3.8639823636363633 -2.8873586363636368 -1.0631172727272726 5.782385764e-3 1.3354113599999998e-2 1.6006551288999997 1.2727103473548094 7.464115552925086 3.9400243636363634 3.0029186363636367 0.20205272727272727 4.969606842975207 4.325445000000001 1.371297 15.523791986048131 9.017520336620043 4.082530459834711e-2 36.90134728909755 34.7171749005811 4.02080485908967 6.0746479148258095 5.892128214879671 2.005194469144993
user_004 Jumping 1.446046453704e12 19.54396 -17.70339 -8.46939 6.215456545454546 -4.782473272727273 -1.665791818181818 381.96637248159993 313.41001749209994 71.73056697210001 27.69669577667704 10.319630313832954 13.328503454545451 12.920916727272726 6.803598181818183 6.116683256198346 5.222330438016529 1.9416679917355373 177.64900433783004 166.95008907311612 46.288948219639686 53.542354201833035 48.31052397701225 8.222915800069886 7.3172641199995665 6.950577240561553 2.8675626933111484
user_004 Jumping 1.446046454158e12 0.652044 1.72501 -1.03525 2.9965505454545447 -2.4797716363636364 -1.2582024545454547 0.42516137793599995 2.9756595001 1.0717425625 2.114843597180652 6.536228248549308 2.3445065454545446 4.204781636363636 0.22295245454545465 4.096157082644628 4.746651454545454 1.610085991735537 5.496710941679202 17.680188609500856 4.970779698784302e-2 26.08166873535217 35.08718337156006 6.381092379007582 5.107021513108416 5.923443539999353 2.5260824173030425
user_004 Jumping 1.446046454222e12 18.04947 -16.01729 -3.67935 4.644324363636363 -3.946394363636364 -1.4776733636363637 325.7833672809 256.55357894409997 13.5376164225 24.410542039198965 8.639667493262413 13.405145636363637 12.070895636363636 2.2016766363636364 4.956622652892562 5.571013 1.7918699834710743 179.69792953211905 145.70652146398265 4.847380011109496 41.00659033044952 47.5134562013203 6.818051897781323 6.403638835103798 6.89300052236472 2.6111399613542976
user_004 Jumping 1.446046454805e12 19.54396 -19.54276 -2.60638 5.665038272727272 -4.580421272727273 -1.592634181818182 381.96637248159993 381.91946841760006 6.793216704400001 27.761106923240654 10.240967917439297 13.878921727272726 14.962338727272728 1.0137458181818182 5.758814264462809 6.220874892561983 1.9220324628099172 192.62446831176294 223.87158018964527 1.027680583881124 52.80492603983436 63.4497100096025 6.33968770209533 7.266699803888582 7.965532625606557 2.5178736469678795
user_004 Jumping 1.44604645487e12 12.22478 -14.1396 -1.03525 6.717105181818181 -6.022658545454546 -1.592634181818182 149.4452460484 199.92828816 1.0717425625 18.720183673535363 11.750544288016998 5.50767481818182 8.116941454545454 0.557384181818182 6.046375016528926 6.576525785123966 1.9524353471074383 30.334481902834142 65.88473857651847 0.3106771261411241 55.06290521812116 67.83194182478591 6.36341218656381 7.420438344068439 8.236014923783584 2.522580461861189
user_004 Jumping 1.446046454999e12 1.11189 5.2888 -2.49142 3.543486090909091 -2.371778181818182 -0.9168032727272727 1.2362993721000002 27.97140544 6.207173616400001 5.951040113165093 8.1335275932362 2.431596090909091 7.660578181818182 1.5746167272727276 4.734616950413223 5.498490280991735 2.0813304214876034 5.912659549324371 58.684458079748765 2.4794178378070755 34.00437737975152 47.93823792689663 7.181088815206571 5.831327239981608 6.923744501849893 2.679755364806006
user_004 Jumping 1.446046455711e12 1.64837 -0.650847 -0.613724 3.362334090909091 -2.775883545454545 -1.0178284545454546 2.7171236568999997 0.42360181740899994 0.37665714817600005 1.8754686407628893 7.188170906724486 1.7139640909090912 2.1250365454545452 0.4041044545454545 4.886314818181819 4.410319421487603 1.4837242231404957 2.937672904925827 4.515780319517387 0.16330041018347932 35.089401725842805 36.05135667432639 3.7502605393120323 5.923630789122733 6.004278197612631 1.9365589428964025
user_004 Jumping 1.446046455906e12 19.54396 -18.43147 -6.89825 5.710325181818182 -4.698866090909092 -1.634437181818182 381.96637248159993 339.7190863609 47.5858530625 27.735740695085102 9.91661877902701 13.833634818181817 13.732603909090908 5.263812818181818 5.815186834710744 5.599832173553719 1.9872717603305785 191.3694522828123 188.5844101239789 27.707725384855213 50.6927278488261 52.74739577233272 6.313621098201948 7.119882572685177 7.262740238527929 2.5126920022561356
user_004 Jumping 1.44604645597e12 8.77595 -13.18159 0.804128 6.455829454545454 -6.026142454545454 -1.568247545454546 77.0172984025 173.7543149281 0.646621840384 15.85617340883304 11.218761559924888 2.3201205454545457 7.155447545454546 2.372375545454546 5.748047107438017 5.903544363636365 2.0901981487603303 5.382959345440299 51.200429575751485 5.628165728670754 50.331598154741954 56.07913702398294 6.685448936200232 7.094476594840662 7.488600471649088 2.585623510142231
user_004 Jumping 1.446046456423e12 15.78857 -13.10495 -5.59536 4.7453508181818185 -3.9289750909090913 -1.4672216363636366 249.2789426449 171.73971450250002 31.308053529600002 21.267973826319235 8.458796354972607 11.04321918181818 9.17597490909091 4.1281383636363636 4.97340756198347 5.5171746115702485 2.1848901239669414 121.952689897677 84.19851553226592 17.041526349326315 38.41901825757145 44.11913462622769 6.885287929257149 6.1983076930377905 6.6422236206128815 2.623983218173689
user_004 Jumping 1.446046457136e12 -0.842448 -0.919089 3.48655 5.034495545454545 -4.845178090909091 -0.8018418181818183 0.7097186327039999 0.8447245899210001 12.1560309025 3.7027657399739726 9.638416900898887 5.876943545454545 3.9260890909090906 4.288391818181818 5.478537785123968 5.961183520661158 2.0959002066115704 34.538465436459845 15.41417554975537 18.39030438624876 46.56730431938241 52.07823859486755 7.056477213755475 6.824024056184328 7.216525382403054 2.6564030593559167
user_004 Standing 1.446046405596e12 0.958607 -9.4262 3.7722e-2 0.9620908181818184 -9.380912727272724 0.17358445454545454 0.918927380449 88.85324643999999 1.422949284e-3 9.47489296877453 9.43237148191842 3.483818181818421e-3 4.528727272727551e-2 0.13586245454545454 6.277442520989121e-2 3.564091774891797e-2 8.517581524662207e-2 1.2136989123968609e-5 2.050937071074632e-3 1.84586065551157e-2 5.963056016724221e-3 1.957546352325278e-3 1.0145801756643359e-2 7.722082631469454e-2 4.424416743849157e-2 0.1007263707111666
user_004 Standing 1.44604640592e12 0.920286 -9.38788 -5.99e-4 0.9551232727272727 -9.380912727272726 7.952554545454546e-2 0.8469263217960001 88.13229089439999 3.5880100000000004e-7 9.432879601425908 9.430044911924979 3.483727272727266e-2 6.967272727273155e-3 8.012454545454546e-2 3.9903884297520766e-2 3.578644628099232e-2 8.487475206611572e-2 1.2136355710743755e-3 4.854288925620431e-5 6.4199427842975216e-3 2.228623198332091e-3 1.6228770476333844e-3 8.828314650245684e-3 4.720829586346124e-2 4.028494815229858e-2 9.395911158714564e-2
user_004 Standing 1.446046406114e12 1.03525 -9.38788 -0.11556 0.9760256363636363 -9.387879999999997 2.7270636363636358e-2 1.0717425625 88.13229089439999 1.3354113599999998e-2 9.445495623338141 9.439062086037746 5.922436363636374e-2 1.7763568394002505e-15 0.14283063636363635 5.573941322314055e-2 3.0085950413224018e-2 9.37422396694215e-2 3.507525248132244e-3 3.1554436208840472e-30 2.040059068404132e-2 4.640469144164543e-3 1.4684224000000342e-3 1.0355239224131481e-2 6.812098901340571e-2 3.8320000000000444e-2 0.10176069587090823
user_004 Standing 1.446046406438e12 1.03525 -9.34956 -3.8919e-2 1.010862818181818 -9.387879999999997 -3.543536363636363e-2 1.0717425625 87.41427219360001 1.514688561e-3 9.40678103522459 9.4424099981621 2.4387181818182002e-2 3.831999999999702e-2 3.4836363636363693e-3 4.4338446280991796e-2 2.786909090909068e-2 6.872322314049586e-2 5.947346370330668e-4 1.4684223999997718e-3 1.2135722314049627e-5 3.3783190145950433e-3 1.381265848835436e-3 6.272074051694967e-3 5.812330870309297e-2 3.7165385089292916e-2 7.919642701343897e-2
user_004 Standing 1.446046409221e12 1.07357 -9.34956 -7.7239e-2 1.0387325454545453 -9.366978181818181 -9.814136363636364e-2 1.1525525448999998 87.41427219360001 5.965863121e-3 9.411311842757152 9.4250039711814 3.4837454545454616e-2 1.741818181818111e-2 2.0902363636363636e-2 2.977019008264463e-2 3.895338842975155e-2 3.2303239669421495e-2 1.2136482392066164e-3 3.0339305785121503e-4 4.369088055867768e-4 1.2335131438189323e-3 2.1502293445529123e-3 1.3349670551164533e-3 3.5121405777943066e-2 4.6370565497445816e-2 3.653720097539566e-2
user_004 Standing 1.446046409545e12 1.03525 -9.46452 -0.15388 1.0457003636363635 -9.366978181818181 -0.12252709090909092 1.0717425625 89.5771388304 2.3679054399999996e-2 9.52221405174763 9.426078761809261 1.0450363636363535e-2 9.754181818181884e-2 3.135290909090907e-2 2.7236024793388432e-2 3.2619504132230824e-2 2.881955371900826e-2 1.092101001322293e-4 9.514406294215004e-3 9.830049084628087e-4 1.2334795734898574e-3 1.730995300976674e-3 9.763978611780614e-4 3.512092785633457e-2 4.1605231653923935e-2 3.1247365667813685e-2
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user_005 Jogging 1.44604653851e12 19.54396 -19.58108 -17.24474 4.961337090909091 -11.714966363636364 -3.1637672727272728 381.96637248159993 383.4186939664 297.38105766760003 32.60009392801806 17.782204210454342 14.582622909090908 7.866113636363636 14.080972727272727 4.728282619834711 6.480883942148759 10.929839975206612 212.652890908743 61.875743740185946 198.2737929461983 47.499936104067004 46.55667068685511 135.73274442981278 6.892019740545365 6.823244879590289 11.650439666802828
user_005 Jogging 1.446046538793e12 -0.574206 -3.98471 -7.28146 -7.8376469090909096 -0.5324014545454545 -6.713621181818181 0.329712530436 15.877913784100002 53.0196597316 8.320293627398975 12.435762186053363 7.263440909090909 3.452308545454546 0.5678388181818192 7.900635925619834 5.027245347107438 2.382826198347108 52.757573839855375 11.918434293018482 0.32244092343412517 85.47902965290235 37.76294123158168 10.598142654253946 9.245486988412365 6.145155915970048 3.2554788671183146
user_005 Jogging 1.446046538826e12 -7.6042e-2 -3.48655 -0.1922 -5.848474090909092 -1.5147946363636364 -3.268276545454546 5.782385764e-3 12.1560309025 3.694084e-2 3.4926714887409607 8.79684486571532 5.772432090909092 1.9717553636363634 3.076076545454546 7.780923876033058 2.39739494214876 3.527368603305786 33.32097224415711 3.8878192140287675 9.462246913495573 76.2985903174133 7.323591190988912 15.880524485696595 8.73490642865814 2.706213441506215 3.9850375764472528
user_005 Jogging 1.446046538939e12 0.11556 -19.58108 -11.68829 10.354050363636363 -19.23271363636363 -4.720963272727273 1.3354113599999998e-2 383.4186939664 136.6161231241 22.804564701043954 23.674808329678697 10.238490363636362 0.3483663636363694 6.967326727272727 8.065950421487605 6.036874181818183 4.233919008264463 104.82668492627465 0.12135912331322717 48.54364172456889 87.57518667666108 52.2563347149799 22.62891425410126 9.358161500885796 7.228854315517771 4.756985837071754
user_005 Jogging 1.446046538947e12 -3.94639 -19.58108 -8.85259 10.044004545454545 -19.570629090909087 -5.835736 15.5739940321 383.4186939664 78.36834970809998 21.84859349492777 24.18511751724803 13.990394545454546 1.0450909090913285e-2 3.0168539999999995 9.269081396694215 5.096284652892563 4.0549853140495875 195.7311395374843 1.0922150082653395e-4 9.101408057315997 105.31697516413934 42.50487940249286 21.1970906556682 10.26240591499573 6.519576627549742 4.604029827843017
user_005 Jogging 1.446046538979e12 -4.9044 -13.48815 4.48288 3.7316036363636367 -18.936601818181813 -4.727930727272727 24.05313936 181.93019042249998 20.0962130944 15.03594170236437 22.799217118887203 8.636003636363636 5.448451818181814 9.210810727272726 10.874417636363638 2.1022333057851252 4.469224636363635 74.58055880728594 29.68562721504872 84.83903425364232 137.5093735084357 9.196215785575577 26.529507394367275 11.726439080489682 3.0325263041852706 5.150680284619428
user_005 Jogging 1.446046539076e12 2.22318 4.714 7.58682 3.337951181818182 8.69234272727273 7.287228181818182 4.9425293124000005 22.221796000000005 57.559837712400004 9.204572940924528 12.255265287128658 1.114771181818182 3.9783427272727288 0.2995918181818187 3.6958518016528927 12.376827272727269 4.278889537190082 1.2427147878123062 15.827210855643813 8.97552575214879e-2 22.984673684992117 209.9559773255614 26.829853647783384 4.794233378235995 14.489857740004261 5.179754207275031
user_005 Jogging 1.446046539133e12 -1.83878 -5.82409 -7.7413 -0.1352640909090908 1.7459141818181818 1.9432845454545458 3.3811118884000004 33.9200243281 59.927725689999995 9.860469659529407 6.6261052838034376 1.7035159090909093 7.570004181818182 9.684584545454545 2.386311570247934 5.564680247933885 4.438188 2.9019664525258273 57.30496331274476 93.79117781805701 7.486160788873022 35.40458672840595 28.815742980033075 2.7360849381685908 5.950175352744316 5.36802971117272
user_005 Jogging 1.446046539158e12 1.91661 -15.67241 -16.55497 -0.5393703636363636 -3.3263021818181815 -4.480592727272727 3.6733938920999996 245.62443520809998 274.06703170090003 22.877168985718054 9.004548610586447 2.4559803636363635 12.346107818181817 12.074377272727274 2.4233651074380167 7.4813292396694235 7.8037251239669425 6.031839546567404 152.4263782581702 145.79058652415293 7.831012942022457 59.9454553949351 78.68714381250022 2.7983947080464646 7.742445052755305 8.870577422721714
user_005 Jogging 1.446046539262e12 -3.29495 -8.96635 8.08499 7.434739363636364 -11.014747272727275 7.938672727272728 10.856695502500001 80.3954323225 65.36706330009999 12.514758931961094 18.097212429523772 10.729689363636364 2.048397272727275 0.14631727272727169 8.480824768595042 2.806567272727272 8.609085867768597 115.12623384013132 4.195931386916539 2.1408744298346803e-2 87.08888798453219 11.310359394584937 97.52241800503252 9.332142732756084 3.363087776818342 9.875343943632167
user_005 Jogging 1.446046539302e12 -5.90073 -19.58108 9.00468 0.3803184545454543 -14.035085454545454 8.408968181818182 34.8186145329 383.4186939664 81.0842619024 22.345504478567943 17.498293291626553 6.281048454545455 5.545994545454546 0.5957118181818188 7.303660975206612 3.1999061157024795 3.8785865289256205 39.451569688347845 30.758055498211576 0.3548725703214884 61.10605286841874 15.428117018312696 27.387089683350485 7.8170360667211165 3.927864180227302 5.233267591414611
user_005 Jogging 1.446046539319e12 -12.76007 -19.58108 -0.996927 -3.8209815454545453 -14.57853727272727 6.8204166363636345 162.81938640490003 383.4186939664 0.993863443329 23.39298920220819 17.550602729410397 8.939088454545455 5.002542727272729 7.817343636363635 8.1581096446281 3.2246087603305784 3.2974476859504134 79.90730239818785 25.025433738189275 61.11086152899502 70.00862498085486 15.035771101271676 18.016229528635986 8.367115690657974 3.877598625602149 4.244552924471079
user_005 Jogging 1.4460465394e12 7.39642 -2.56686 -6.85994 2.146536363636363 -14.540217272727268 -8.664473363636363 54.7070288164 6.5887702596 47.0587768036 10.409350406226125 20.08056548210617 5.249883636363637 11.973357272727268 1.804533363636363 8.138792611570247 5.077282975206612 7.848378743801652 27.561278195358685 143.36128438037096 3.256340660476766 80.77637375669282 37.16680859849946 89.7226633472793 8.987567733079558 6.0964586932496685 9.472204777520348
user_005 Jogging 1.446046539578e12 7.20482 -19.58108 -5.59536 0.6590119999999999 -9.952231818181817 -0.3907689090909091 51.909431232399996 383.4186939664 31.308053529600002 21.601763324515893 10.362929003394015 6.545808 9.628848181818183 5.2045910909090916 4.776739033057851 5.110536950413223 4.43185461983471 42.847602372864 92.71471730850334 27.08776842357029 36.474788326173105 42.3693493244356 21.37286011656183 6.039436093392586 6.509174242900216 4.623079073146147
user_005 Jogging 1.44604653961e12 19.16076 -19.58108 1.83878 6.097012454545455 -15.902331818181814 -1.007377 367.1347237776 383.4186939664 3.3811118884000004 27.457868264532117 18.36803215234251 13.063747545454545 3.678748181818186 2.846157 5.852874611570247 6.96764694214876 3.697434991735537 170.66149993136963 13.533188185230609 8.100609668649 58.13333525565159 55.72582041356123 16.12637407479173 7.624521968992652 7.464972901060072 4.0157656897273935
user_005 Jogging 1.446046539682e12 -6.89706 -9.50284 5.67081 3.9998454545454547 -18.448889090909088 -3.1358959090909093 47.5694366436 90.30396806560002 32.158086056100004 13.03961237020871 22.043440536798908 10.896905454545454 8.946049090909087 8.80670590909091 10.263827082644628 2.2273277685950434 3.8956880661157025 118.74254848530246 80.0317943369553 77.55806896921675 120.76651362675165 12.032200956290906 21.95008966866136 10.989381858264442 3.468746309012942 4.685092279631358
user_005 Jogging 1.446046539755e12 0.345482 6.70665 10.88237 0.10510900000000002 6.06217509090909 8.36019909090909 0.119357812324 44.9791542225 118.4259768169 12.78766940656991 14.882968130300673 0.240373 0.6444749090909099 2.52217090909091 4.839443595041322 13.605293487603307 7.101292727272729 5.7779179129e-2 0.41534790844773656 6.361346094664468 39.33490163750689 251.6198299976049 54.91098830766759 6.27175427113554 15.862529117313068 7.4101948899922725
user_005 Jogging 1.446046539772e12 1.87829 3.87095 8.73643 1.6692744545454545 9.27411418181818 9.293819999999998 3.5279733241 14.9842539025 76.3252091449 9.738451436008704 13.889704399334034 0.20901554545454548 5.403164181818179 0.557389999999998 2.7011064214876033 13.387405983471075 5.818670082644629 4.368749824166117e-2 29.19418317568291 0.31068361209999784 12.831011815806407 247.56773383231638 43.55359550083381 3.5820401750687285 15.734285297792091 6.599514792833926
user_005 Jogging 1.446046539942e12 0.537083 -9.27292 11.76374 6.584724818181818 -9.77108 4.778988181818182 0.28845814888899995 85.98704532639998 138.3855787876 14.988698484621304 16.5450927999781 6.047641818181818 0.4981600000000004 6.9847518181818185 4.964222173553718 3.317082661157025 9.577860421487607 36.57397156102149 0.24816338560000037 48.78675796159422 37.43595558677263 25.67015866625859 127.0524021856521 6.118492917931068 5.066572674526498 11.271752400831563
user_005 Jogging 1.44604653999e12 -0.804128 -19.58108 5.40257 3.156799727272728 -10.986879090909092 7.015502727272727 0.646621840384 383.4186939664 29.187762604899998 20.32862706657004 15.52897945485492 3.960927727272728 8.594200909090908 1.6129327272727272 5.840839694214875 2.2605820661157026 4.385301074380165 15.6889484606779 73.86028926581899 2.601551982707438 40.40700436750239 12.320737220128098 25.776883221690685 6.356650404694472 3.5100907709243216 5.077093974085046
user_005 Jogging 1.446046540096e12 9.8106 0.613724 -5.25048 2.376456363636364 -13.192039636363639 -8.350943636363636 96.24787236000002 0.37665714817600005 27.567540230399995 11.14414957448867 18.601696635532743 7.434143636363637 13.805763636363638 3.100463636363637 6.501151743801654 6.114781735537192 7.790107867768596 55.266491606085964 190.59910958314055 9.612874760413225 54.78844817181653 52.86952324153863 86.24638087968755 7.401921924190806 7.271143186703081 9.286892961571569
user_005 Jogging 1.446046540104e12 2.2615 1.95493 -8.66099 3.400654545454545 -11.234220545454544 -8.657506363636365 5.114382249999999 3.8217513049000003 75.0127477801 9.16236221369795 17.417205621653796 1.139154545454545 13.189150545454545 3.483636363634801e-3 5.963083008264463 6.803280214876033 6.720622851239669 1.2976730784297512 173.95369211066392 1.21357223140387e-5 50.3778636743197 65.81661171360078 73.65714927952101 7.097736517673765 8.112743784540516 8.582374338114192
user_005 Jogging 1.44604654012e12 -13.06663 -2.2603 -14.94552 2.752692727272727 -7.8515822727272715 -9.939495454545453 170.7368195569 5.1089560899999995 223.36856807040002 19.98034893882737 16.54993111786699 15.819322727272727 5.5912822727272715 5.006024545454547 7.762237380165288 7.418938900826446 5.458586206611571 250.25097154960744 31.26243745331424 25.060281749693406 79.2542529639753 73.90660281971599 53.25524326196705 8.902485774432627 8.59689495223223 7.297619013210203
user_005 Jogging 1.446046540161e12 -9.69444 -3.14167 -4.9056 -5.496624545454545 -2.319522272727273 -7.13166090909091 93.9821669136 9.8700903889 24.064911359999996 11.310047244043679 15.320231174103524 4.197815454545455 0.8221477272727271 2.2260609090909105 11.350412371900823 6.493867867768595 3.1625339338842977 17.621654590420665 0.6759268854597105 4.955347170982651 159.6364509893014 66.65468024152023 12.332436510854023 12.63473193183383 8.164231760644734 3.5117568980289655
user_005 Jogging 1.446046540193e12 1.41845 -3.52487 -0.307161 -9.380910363636366 -3.1695362727272727 -7.542732818181817 2.0120004025 12.424708516899999 9.434787992100001e-2 3.8119623292106386 14.2613084737019 10.799360363636366 0.3553337272727273 7.235571818181818 10.904820504132232 2.8220859338842974 3.948575702479339 116.62618426368017 0.12626205773752894 52.353499536066934 151.38493928892447 15.014976780614317 17.83407046135147 12.303858715416252 3.8749163578862342 4.22304042857175
user_005 Jogging 1.446046540217e12 -2.29862 -6.7821 0.689167 -6.287415909090908 -4.050903636363636 -3.8430809090909097 5.2836539044 45.996880409999996 0.47495115388899994 7.1941285412681495 10.53827068016974 3.9887959090909075 2.7311963636363634 4.53224790909091 8.581849818181821 1.5055775371900824 4.445789826446281 15.91049280438036 7.459433576740495 20.541271109458926 97.87125545609757 3.4811748435715795 22.0431335507613 9.892990218134129 1.865790675175428 4.695011560237238
user_005 Jogging 1.446046540339e12 -2.10702 -19.58108 -9.65732 9.967363636363636 -19.52882545454545 -3.710701727272727 4.439533280399999 383.4186939664 93.2638295824 21.934494679139522 23.708771637632957 12.074383636363637 5.225454545454866e-2 5.946618272727273 9.42869852892562 4.468590330578514 4.145560669421488 145.79074019808596 2.730537520661492e-3 35.36226888153389 110.2410839227691 32.07802959815651 22.283775548103858 10.499575416309419 5.6637469574616865 4.720569409308993
user_005 Jogging 1.446046540514e12 5.13552 -2.79678 -7.89458 -2.3648110000000004 -7.228005272727272 -7.724018181818183e-2 26.373565670399998 7.8219783684 62.3243933764 9.824456087499195 16.574582047151853 7.500331 4.431225272727272 7.817339818181819 9.026175991735538 8.765533487603308 6.827665371900826 56.254965109561 19.63575741765689 61.11080183293095 104.91049056532684 167.91245901524488 57.88847597379886 10.2425822215556 12.958103989984217 7.608447671752685
user_005 Jogging 1.446046540563e12 4.33079 -10.57581 1.14901 1.3104566363636363 -4.935754363636364 -4.727931818181819 18.7557420241 111.84775715610002 1.3202239801000002 11.485805290022116 16.556757493226794 3.020333363636364 5.640055636363637 5.87694181818182 3.7015521404958673 12.720126892561984 11.229750066115702 9.122413627494954 31.81022758127723 34.53844513429423 17.429640415905563 216.0190225727229 139.08506675730447 4.174882084072023 14.697585603517432 11.793433204851947
user_005 Jogging 1.446046540717e12 -6.51385 -19.58108 -6.43841 -2.0268926363636366 -14.97219181818182 4.50029790909091 42.430241822499994 383.4186939664 41.453123328100006 21.61717046972152 17.517867329058955 4.486957363636363 4.60888818181818 10.93870790909091 5.2619166611570245 3.4266593388429754 4.726067801652892 20.132786383090583 21.241850272503292 119.65533072040803 35.40847356546848 18.10052390631796 32.93065278676701 5.950501959118111 4.254471048945798 5.738523572032009
user_005 Jogging 1.446046540725e12 -2.2603 -19.58108 -13.68095 -1.9711544545454545 -15.864009999999997 2.100052454545455 5.1089560899999995 383.4186939664 187.1683929025 23.993666726011263 18.21761657089616 0.2891455454545455 3.717070000000003 15.781002454545455 4.390684289256199 3.647080165289256 5.649555338842974 8.360514645620665e-2 13.816609384900024 249.04003847036967 26.55514334235929 19.20472444099144 52.69664942424522 5.153168281975594 4.382319527486722 7.259245788940144
user_005 Jogging 1.446046540822e12 -17.85667 -2.6435 -5.71033 -1.9746390909090907 -4.9949745454545456 -9.228827272727273 318.86066348890006 6.98809225 32.6078687089 18.932950759134194 15.92906364072432 15.882030909090911 2.3514745454545456 3.5184972727272728 9.308036595041322 7.645694710743802 3.4605465619834708 252.2389057973191 5.529432537920662 12.379823058189256 111.33812321837313 84.26482645558666 22.85573815537694 10.551688169121238 9.179587488312677 4.780767527853341
user_005 Jogging 1.44604654083e12 -14.75272 -3.21831 -3.14286 -3.5666736363636358 -3.8558163636363645 -8.222047272727272 217.6427473984 10.357519256099998 9.877568979600001 15.42328874248615 15.386100335157364 11.186046363636365 0.6375063636363647 5.079187272727271 9.959798661157025 7.569370495867769 2.874974024793388 125.12763324942235 0.40641436367686085 25.798143351434696 121.24667289853097 84.10343286667974 13.135425839644437 11.011206695840878 9.170792379433728 3.6242828034860133
user_005 Jogging 1.446046540838e12 -10.72909 -3.25663 -3.2195 -4.94968909090909 -2.831619090909091 -7.389450909090908 115.11337222809999 10.6056389569 10.36518025 11.66551290921235 14.664609256904741 5.779400909090909 0.4250109090909091 4.169950909090908 9.997485338842976 7.3236142148760335 2.544659586776859 33.401474868000825 0.180634272846281 17.388490584228094 121.66666259466352 83.23017701976578 9.180451949874753 11.030261220599607 9.123057438148999 3.029926063433686
user_005 Jogging 1.446046540927e12 -4.40624 -6.24561 0.497565 -2.3682936363636364 -2.302102909090909 -3.285694818181818 19.414950937600004 39.0076442721 0.24757092922499999 7.659645301117083 5.719106541657371 2.037946363636364 3.9435070909090912 3.783259818181818 4.485693983471075 1.3127083719008263 3.0485239504132235 4.153225381058679 15.551248176050283 14.313054851869122 31.307420746990218 2.6778831208879526 12.53341286799084 5.595303454415159 1.6364238817885641 3.540256045541175
user_005 Jogging 1.446046540976e12 3.2195 -19.58108 -6.9749 -3.1137981818181815 -10.206538636363636 -1.711078363636364 10.36518025 383.4186939664 48.64923001 21.034093853227905 11.824510576150509 6.333298181818181 9.374541363636364 5.263821636363636 2.541175404958677 6.296882462809918 2.6064149173553717 40.11066585982148 87.88202577852914 27.707818219449944 9.363454819684579 59.75930482568696 9.126199517026393 3.0599762776342856 7.730414272578602 3.020960032345081
user_005 Jogging 1.446046541161e12 3.33447 3.52607 4.7128 1.1502080909090908 9.664284545454546 10.102029090909092 11.1186901809 12.4331696449 22.21048384 6.764787037727056 14.376779236094603 2.1842619090909094 6.138214545454546 5.389229090909092 3.1340315041322313 12.022759669421486 6.602495041322314 4.771000087505464 37.677677806029756 29.04379019430084 12.984159483889442 206.43890998226743 57.00039092718214 3.60335392154163 14.367982112400734 7.549860325011459
user_005 Jogging 1.446046541235e12 7.58802 -19.58108 -19.54396 2.4949023636363634 -3.928976181818182 -3.397172818181819 57.578047520400006 383.4186939664 381.96637248159993 28.687333685241644 9.474860604030479 5.093117636363637 15.652103818181818 16.14678718181818 2.017675578512397 8.562214818181818 8.995139520661157 25.93984725783832 244.98835393514184 260.71873629492785 5.3878930097540945 83.3646869357908 98.37953343385551 2.3211835364214726 9.130426437784317 9.918645745960257
user_005 Jogging 1.44604654134e12 -1.57053 -7.85507 11.4955 3.2404081818181822 -8.774752727272727 9.586448181818183 2.4665644809 61.70212470490001 132.14652025 14.011252957383933 15.148865444150692 4.810938181818182 0.9196827272727264 1.9090518181818172 4.711814024793388 2.3742772479338843 9.278583223140494 23.145126189276034 0.8458163188438 3.6444788445033023 27.561006826288562 7.091636321663279 126.34631874555262 5.249857791053826 2.66301264016213 11.240387837861851
user_005 Jogging 1.446046541412e12 6.59169 -19.58108 -15.52033 -1.9885736363636364 -16.68963909090909 -0.8122936363636359 43.450377056099995 383.4186939664 240.88064330889998 25.840853591385095 21.518287655835156 8.580263636363636 2.8914409090909103 14.708036363636364 5.809802975206612 4.999057190082644 9.224111239669421 73.62092406950413 8.36043053076447 216.32633367404958 39.01585487781022 30.637290880641924 151.2366587860819 6.246267275566281 5.535096284676711 12.297831466810802
user_005 Jogging 1.446046541517e12 -9.6178 -1.57053 -5.4804 -3.333268181818182 -1.7725854545454547 -7.323260909090909 92.50207684000002 2.4665644809 30.034784160000005 11.180493078612411 16.414639162431733 6.284531818181819 0.2020554545454547 1.8428609090909083 12.730577107438016 7.694465123966942 2.576645785123967 39.49534017373968 4.082640671157031e-2 3.396136330255369 192.85396690384394 111.32270980228442 8.473371684080687 13.887187148729721 10.550957767060032 2.9109056467155865
user_005 Jogging 1.446046541526e12 -5.24928 -1.37893 -9.619 -4.914851818181818 -0.8563818181818182 -7.8109745454545445 27.5549405184 1.9014479449 92.525161 11.044525769054097 15.8521414573526 0.3344281818181818 0.5225481818181817 1.8080254545454553 11.748183140495867 7.11649305785124 2.5883634710743793 0.11184220879421486 0.27305660232148754 3.2689560442843 181.58080947832903 107.04410449639293 8.514233087209762 13.475192372590792 10.34621208444873 2.9179158807631453
user_005 Jogging 1.446046541542e12 0.307161 -2.68182 -3.87095 -7.788874454545455 -1.243068181818182 -8.194178181818181 9.434787992100001e-2 7.192158512400001 14.9842539025 4.719190639804775 14.113925983821156 8.096035454545456 1.4387518181818182 4.32322818181818 10.823112314049588 4.726066363636364 2.2985858677685944 65.54579008125705 2.0700067943214875 18.69030191206693 164.5818232479703 64.88748743124589 7.553243462068892 12.828944744131151 8.055276992831836 2.74831647778579
user_005 Jogging 1.446046541567e12 -3.67815 -1.53221 -1.64837 -7.945640363636364 -2.6504690909090916 -5.546593272727272 13.5287874225 2.3476674841 2.7171236568999997 4.312027198835833 10.93577854426604 4.267490363636364 1.1182590909090915 3.8982232727272725 10.032953694214877 1.1673442975206612 3.1954715867768595 18.211474003729226 1.2505033944008277 15.196144684032527 144.1504480278062 1.9028610728867017 14.167965256505674 12.006267031338517 1.3794423050228313 3.7640357671660976
user_005 Jogging 1.44604654159e12 -7.3569 -7.81675 3.94639 -4.799891272727273 -2.9047763636363633 -2.5401904545454546 54.123977610000004 61.1015805625 15.5739940321 11.436763187397036 7.609283436074653 2.557008727272727 4.911973636363637 6.486580454545455 5.092799677685949 1.3836494214876036 4.091723454545454 6.538293631348892 24.12748500433141 42.075725993291115 37.86155491294937 3.7278022676234417 20.641978826787177 6.153174376933371 1.9307517364030626 4.543344453900362
user_005 Jogging 1.446046541647e12 12.68462 -19.58108 -8.23947 -3.9045899999999993 -12.63116909090909 1.8352919090909092 160.89958454440003 383.4186939664 67.88886588090001 24.742820057376242 15.833626551545684 16.58921 6.94991090909091 10.07476190909091 5.423432454545455 6.832734214876033 5.923814867768595 275.20188842410005 48.301261644300844 101.50082752486911 49.805850736926025 62.230873282694525 41.61672815250327 7.057326033061391 7.888654719449605 6.451102863270998
user_005 Jogging 1.446046541704e12 -0.804128 -19.58108 -2.03158 8.35791109090909 -19.581079999999996 -0.5022472727272728 0.646621840384 383.4186939664 4.127317296399999 19.70260472889775 23.37056805963689 9.16203909090909 3.552713678800501e-15 1.529332727272727 10.50261695041322 4.642774710743804 4.055621512396694 83.94296030334627 1.2621774483536189e-29 2.3388585907074373 146.0293819989988 37.699085315443725 26.298503606760573 12.084261748199548 6.1399580874338 5.128206665761489
user_005 Jogging 1.446046541777e12 2.4531 19.54396 15.94065 -1.5252460909090912 -3.827948181818181 6.688037636363637 6.01769961 381.96637248159993 254.1043224225 25.33946318519988 18.518431558431583 3.978346090909091 23.371908181818178 9.252612363636363 7.945605561983471 11.386833388429755 5.833873041322314 15.827237619051646 546.2460920593395 85.61083555171648 88.88585335568426 235.8254541440598 43.61219456277871 9.42792943098771 15.356609461207894 6.603952949770214
  1. Prediction using Random Forests

Instead of just stopping at ETL and feature engineering let's quickly see without understanding the random forest model in detail how simple it is to predict the activity using random forest model.

val splits = dataFeatDF.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
splits: Array[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = Array([user_id: string, activity: string ... 27 more fields], [user_id: string, activity: string ... 27 more fields])
trainingData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
testData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
import org.apache.spark.ml.feature.{StringIndexer,VectorAssembler}
import org.apache.spark.ml.Pipeline

import org.apache.spark.ml.classification.RandomForestClassifier

val transformers = Array(
              new StringIndexer().setInputCol("activity").setOutputCol("label"),
              new VectorAssembler()
                      .setInputCols(Array("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ"))
                      .setOutputCol("features")
)

// Train a RandomForest model.
val rf = new RandomForestClassifier() 
              .setLabelCol("label")
              .setFeaturesCol("features")
              .setNumTrees(10)
              .setFeatureSubsetStrategy("auto")
              .setImpurity("gini")
              .setMaxDepth(20)
              .setMaxBins(32)
              .setSeed(12345)

val model = new Pipeline().setStages(transformers :+ rf).fit(trainingData)
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
transformers: Array[org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable}}] = Array(strIdx_84996728c97a, VectorAssembler: uid=vecAssembler_e11ceeee69a9, handleInvalid=error, numInputCols=6)
rf: org.apache.spark.ml.classification.RandomForestClassifier = rfc_6ab6b8d21734
model: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c

Before we go further let's find quickly which label is mapped to which activity by the StringIndexer and quickly check that the estimator we have built can transform the union of the training and test data. This is mostly for debugging... not something you would do in a real pipeline except during debugging stages.

val activityLabelDF = model.transform(trainingData.union(testData)).select("activity","label").distinct
display(activityLabelDF) // this just shows what activity is associated with what label
activity label
Jogging 0.0
Jumping 4.0
Standing 1.0
Walking 2.0
Sitting 3.0
val myPredictionsOnTestData = model.transform(testData)
display(myPredictionsOnTestData)

Let's evaluate accuracy at a lower level...

val accuracy: Double = 1.0 * model.transform(testData)
                                  .select("activity","label","prediction")
                                  .filter($"label"===$"prediction").count() / testData.count() 
accuracy: Double = 0.9828725226327379

We get 98% correct predictions and here are the mis-predicted ones.

display(model.transform(testData).select("activity","label","prediction").filter(not($"label"===$"prediction")))
activity label prediction
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Walking 2.0 1.0
Walking 2.0 1.0
Walking 2.0 1.0
Jogging 0.0 2.0
Standing 1.0 2.0
Standing 1.0 2.0
Standing 1.0 2.0
Standing 1.0 2.0
Standing 1.0 2.0
Jumping 4.0 2.0
Walking 2.0 1.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jumping 4.0 2.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Jogging 0.0 2.0
Jogging 0.0 2.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 2.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jogging 0.0 4.0
Jumping 4.0 2.0
Jumping 4.0 0.0
Jumping 4.0 0.0
Walking 2.0 0.0
Walking 2.0 1.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
Walking 2.0 0.0
testData.printSchema()
root
 |-- user_id: string (nullable = true)
 |-- activity: string (nullable = true)
 |-- timeStampAsLong: long (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)
 |-- meanX: double (nullable = true)
 |-- meanY: double (nullable = true)
 |-- meanZ: double (nullable = true)
 |-- SqX: double (nullable = true)
 |-- SqY: double (nullable = true)
 |-- SqZ: double (nullable = true)
 |-- resultant: double (nullable = true)
 |-- meanResultant: double (nullable = true)
 |-- absDevFromMeanX: double (nullable = true)
 |-- absDevFromMeanY: double (nullable = true)
 |-- absDevFromMeanZ: double (nullable = true)
 |-- meanAbsDevFromMeanX: double (nullable = true)
 |-- meanAbsDevFromMeanY: double (nullable = true)
 |-- meanAbsDevFromMeanZ: double (nullable = true)
 |-- sqrDevFromMeanX: double (nullable = true)
 |-- sqrDevFromMeanY: double (nullable = true)
 |-- sqrDevFromMeanZ: double (nullable = true)
 |-- varianceX: double (nullable = true)
 |-- varianceY: double (nullable = true)
 |-- varianceZ: double (nullable = true)
 |-- stddevX: double (nullable = true)
 |-- stddevY: double (nullable = true)
 |-- stddevZ: double (nullable = true)

Let's understand the mis-predicitons better by seeing the user_id also.

display(model.transform(testData).select("user_id","activity","label","prediction").filter(not($"label"===$"prediction")))
user_id activity label prediction
user_001 Jogging 0.0 4.0
user_001 Jogging 0.0 4.0
user_001 Jogging 0.0 4.0
user_001 Jogging 0.0 4.0
user_001 Jogging 0.0 4.0
user_001 Jogging 0.0 4.0
user_001 Jumping 4.0 0.0
user_001 Jumping 4.0 0.0
user_001 Jumping 4.0 0.0
user_001 Jumping 4.0 0.0
user_001 Jumping 4.0 0.0
user_001 Jumping 4.0 0.0
user_001 Walking 2.0 1.0
user_001 Walking 2.0 1.0
user_001 Walking 2.0 1.0
user_002 Jogging 0.0 2.0
user_002 Standing 1.0 2.0
user_002 Standing 1.0 2.0
user_002 Standing 1.0 2.0
user_002 Standing 1.0 2.0
user_002 Standing 1.0 2.0
user_003 Jumping 4.0 2.0
user_003 Walking 2.0 1.0
user_004 Jogging 0.0 4.0
user_004 Jogging 0.0 4.0
user_004 Jumping 4.0 2.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Jumping 4.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_004 Walking 2.0 0.0
user_005 Jogging 0.0 2.0
user_005 Jogging 0.0 2.0
user_005 Jogging 0.0 4.0
user_005 Jogging 0.0 4.0
user_005 Jogging 0.0 2.0
user_005 Jogging 0.0 4.0
user_005 Jogging 0.0 4.0
user_005 Jogging 0.0 4.0
user_005 Jumping 4.0 2.0
user_005 Jumping 4.0 0.0
user_005 Jumping 4.0 0.0
user_005 Walking 2.0 0.0
user_006 Walking 2.0 1.0
user_006 Walking 2.0 0.0
user_006 Walking 2.0 0.0
user_006 Walking 2.0 0.0
user_006 Walking 2.0 0.0
user_006 Walking 2.0 0.0

Let's evaluate our model's accuracy using ML pipeline's evaluation.MulticlassClassificationEvaluator:

import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
  .setLabelCol("label")
  .setPredictionCol("prediction")
  .setMetricName("accuracy")
val accuracy = evaluator.evaluate(myPredictionsOnTestData)
println(s"Test Error = ${(1.0 - accuracy)}")
Test Error = 0.017127477367262056
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_0923535d6e49, metricName=accuracy, metricLabel=0.0, beta=1.0, eps=1.0E-15
accuracy: Double = 0.9828725226327379

Note, it's the same 98% we got from our slightly lower-level operations earlier for accuracy.

Think!

What else do you need to investigate to try to understand the mis-predicitons better?

  • time-line?
  • are mis-predictions happening during transitions between users or activities of the same user or both, as our Markov process simply uses the same sliding window over the time-odered, user-grouped, activity streams...
  • ...

Typically, in inustry you will have to improve on an existing algorithm that is in production and getting into the details under the given constraints needs some thinking from scratch over the entire data science pipeline and process.

Explaining the Model's Predictions to a Curious Client whose Activity is being Predicted

Now, let's try to explain the decision branches in each tree of our best-fitted random forest model. Often, being able to "explain" why the model "predicts" is critical for various businesses. how it predicts.

import org.apache.spark.ml.classification.RandomForestClassificationModel
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println(s"Learned classification forest model:\n ${rfModel.toDebugString}")
Learned classification forest model:
 RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6
  Tree 0 (weight 1.0):
    If (feature 3 <= 0.45832723868728054)
     If (feature 1 <= -8.097179727272728)
      If (feature 0 <= -7.121753363636364)
       Predict: 2.0
      Else (feature 0 > -7.121753363636364)
       If (feature 5 <= 0.7848413904200169)
        If (feature 1 <= -8.4067287012987)
         If (feature 1 <= -11.596521363636366)
          Predict: 2.0
         Else (feature 1 > -11.596521363636366)
          If (feature 1 <= -9.49238863636364)
           If (feature 0 <= -1.976380909090909)
            Predict: 2.0
           Else (feature 0 > -1.976380909090909)
            Predict: 1.0
          Else (feature 1 > -9.49238863636364)
           Predict: 1.0
        Else (feature 1 > -8.4067287012987)
         If (feature 0 <= -3.502226363636364)
          Predict: 2.0
         Else (feature 0 > -3.502226363636364)
          Predict: 1.0
       Else (feature 5 > 0.7848413904200169)
        Predict: 2.0
     Else (feature 1 > -8.097179727272728)
      If (feature 2 <= 2.943098181818182)
       If (feature 4 <= 0.904496986362019)
        If (feature 3 <= 0.027620762421071275)
         If (feature 0 <= -3.502226363636364)
          Predict: 0.0
         Else (feature 0 > -3.502226363636364)
          If (feature 2 <= -0.045886318181818195)
           If (feature 1 <= -6.820416818181818)
            Predict: 4.0
           Else (feature 1 > -6.820416818181818)
            Predict: 0.0
          Else (feature 2 > -0.045886318181818195)
           Predict: 4.0
        Else (feature 3 > 0.027620762421071275)
         Predict: 4.0
       Else (feature 4 > 0.904496986362019)
        Predict: 2.0
      Else (feature 2 > 2.943098181818182)
       If (feature 3 <= 0.2198889879412786)
        If (feature 0 <= -1.105464818181818)
         Predict: 2.0
        Else (feature 0 > -1.105464818181818)
         If (feature 3 <= 0.12199305012399356)
          Predict: 3.0
         Else (feature 3 > 0.12199305012399356)
          If (feature 1 <= -0.6543302727272726)
           Predict: 3.0
          Else (feature 1 > -0.6543302727272726)
           Predict: 2.0
       Else (feature 3 > 0.2198889879412786)
        If (feature 0 <= -0.1735843181818182)
         Predict: 2.0
        Else (feature 0 > -0.1735843181818182)
         Predict: 3.0
    Else (feature 3 > 0.45832723868728054)
     If (feature 4 <= 2.2658035906051546)
      If (feature 5 <= 2.8353340431585474)
       If (feature 5 <= 0.7848413904200169)
        If (feature 1 <= -8.097179727272728)
         If (feature 3 <= 1.5843984037196344)
          If (feature 0 <= -8.90539)
           Predict: 0.0
          Else (feature 0 > -8.90539)
           If (feature 0 <= 2.8563333333333336)
            If (feature 0 <= -4.357466363636364)
             If (feature 1 <= -8.4067287012987)
              If (feature 2 <= -1.0073777272727273)
               Predict: 1.0
              Else (feature 2 > -1.0073777272727273)
               Predict: 2.0
             Else (feature 1 > -8.4067287012987)
              Predict: 1.0
            Else (feature 0 > -4.357466363636364)
             If (feature 0 <= -2.5093826818181824)
              If (feature 2 <= -0.5126976818181816)
               Predict: 1.0
              Else (feature 2 > -0.5126976818181816)
               Predict: 2.0
             Else (feature 0 > -2.5093826818181824)
              Predict: 1.0
           Else (feature 0 > 2.8563333333333336)
            If (feature 4 <= 0.904496986362019)
             Predict: 1.0
            Else (feature 4 > 0.904496986362019)
             Predict: 2.0
         Else (feature 3 > 1.5843984037196344)
          If (feature 0 <= -8.90539)
           Predict: 0.0
          Else (feature 0 > -8.90539)
           Predict: 2.0
        Else (feature 1 > -8.097179727272728)
         If (feature 3 <= 2.037322122633077)
          If (feature 1 <= -6.820416818181818)
           If (feature 4 <= 0.904496986362019)
            Predict: 4.0
           Else (feature 4 > 0.904496986362019)
            Predict: 2.0
          Else (feature 1 > -6.820416818181818)
           Predict: 2.0
         Else (feature 3 > 2.037322122633077)
          Predict: 0.0
       Else (feature 5 > 0.7848413904200169)
        If (feature 3 <= 3.2305826019123103)
         If (feature 0 <= -8.90539)
          Predict: 0.0
         Else (feature 0 > -8.90539)
          If (feature 1 <= -17.407273636363634)
           Predict: 0.0
          Else (feature 1 > -17.407273636363634)
           If (feature 1 <= -2.2306882272727275)
            If (feature 0 <= -0.5846568636363636)
             If (feature 4 <= 0.904496986362019)
              If (feature 2 <= 0.41744104545454547)
               If (feature 5 <= 1.6130554927490437)
                Predict: 2.0
               Else (feature 5 > 1.6130554927490437)
                If (feature 3 <= 1.7795641834853977)
                 Predict: 2.0
                Else (feature 3 > 1.7795641834853977)
                 If (feature 1 <= -9.105701681818182)
                  If (feature 5 <= 1.7614806708061252)
                   Predict: 2.0
                  Else (feature 5 > 1.7614806708061252)
                   If (feature 5 <= 1.9145738226248412)
                    If (feature 3 <= 2.283530423945612)
                     Predict: 2.0
                    Else (feature 3 > 2.283530423945612)
                     Predict: 1.0
                   Else (feature 5 > 1.9145738226248412)
                    Predict: 2.0
                 Else (feature 1 > -9.105701681818182)
                  If (feature 0 <= -3.065027181818182)
                   If (feature 0 <= -4.988009090909092)
                    Predict: 2.0
                   Else (feature 0 > -4.988009090909092)
                    If (feature 0 <= -3.502226363636364)
                     Predict: 1.0
                    Else (feature 0 > -3.502226363636364)
                     Predict: 2.0
                  Else (feature 0 > -3.065027181818182)
                   Predict: 1.0
              Else (feature 2 > 0.41744104545454547)
               Predict: 2.0
             Else (feature 4 > 0.904496986362019)
              Predict: 2.0
            Else (feature 0 > -0.5846568636363636)
             If (feature 3 <= 1.0884225728208126)
              If (feature 2 <= -0.045886318181818195)
               Predict: 2.0
              Else (feature 2 > -0.045886318181818195)
               Predict: 1.0
             Else (feature 3 > 1.0884225728208126)
              If (feature 4 <= 0.904496986362019)
               If (feature 1 <= -9.49238863636364)
                Predict: 2.0
               Else (feature 1 > -9.49238863636364)
                If (feature 3 <= 1.7795641834853977)
                 If (feature 0 <= 0.32806340909090914)
                  Predict: 2.0
                 Else (feature 0 > 0.32806340909090914)
                  Predict: 1.0
                Else (feature 3 > 1.7795641834853977)
                 Predict: 1.0
              Else (feature 4 > 0.904496986362019)
               Predict: 2.0
           Else (feature 1 > -2.2306882272727275)
            If (feature 2 <= 1.1333349545454543)
             If (feature 0 <= 0.32806340909090914)
              If (feature 1 <= 1.425418909090909)
               Predict: 0.0
              Else (feature 1 > 1.425418909090909)
               Predict: 4.0
             Else (feature 0 > 0.32806340909090914)
              Predict: 0.0
            Else (feature 2 > 1.1333349545454543)
             Predict: 2.0
        Else (feature 3 > 3.2305826019123103)
         If (feature 2 <= -0.9551237727272728)
          If (feature 5 <= 1.4557098485258626)
           If (feature 5 <= 1.2559600735949519)
            Predict: 0.0
           Else (feature 5 > 1.2559600735949519)
            If (feature 0 <= -8.90539)
             Predict: 0.0
            Else (feature 0 > -8.90539)
             Predict: 1.0
          Else (feature 5 > 1.4557098485258626)
           If (feature 3 <= 4.092746328718364)
            If (feature 4 <= 1.9510708064802356)
             If (feature 1 <= -11.596521363636366)
              Predict: 0.0
             Else (feature 1 > -11.596521363636366)
              If (feature 4 <= 1.278422893229283)
               Predict: 2.0
              Else (feature 4 > 1.278422893229283)
               Predict: 0.0
            Else (feature 4 > 1.9510708064802356)
             Predict: 2.0
           Else (feature 3 > 4.092746328718364)
            Predict: 0.0
         Else (feature 2 > -0.9551237727272728)
          If (feature 0 <= 2.8563333333333336)
           If (feature 4 <= 0.904496986362019)
            If (feature 3 <= 3.5937950153207483)
             Predict: 1.0
            Else (feature 3 > 3.5937950153207483)
             Predict: 0.0
           Else (feature 4 > 0.904496986362019)
            If (feature 0 <= -8.90539)
             Predict: 0.0
            Else (feature 0 > -8.90539)
             If (feature 5 <= 2.600144428803959)
              Predict: 2.0
             Else (feature 5 > 2.600144428803959)
              If (feature 4 <= 1.9510708064802356)
               Predict: 0.0
              Else (feature 4 > 1.9510708064802356)
               Predict: 2.0
          Else (feature 0 > 2.8563333333333336)
           If (feature 3 <= 3.5937950153207483)
            If (feature 0 <= 3.5330349999999995)
             Predict: 2.0
            Else (feature 0 > 3.5330349999999995)
             Predict: 0.0
           Else (feature 3 > 3.5937950153207483)
            Predict: 0.0
      Else (feature 5 > 2.8353340431585474)
       If (feature 3 <= 1.0884225728208126)
        If (feature 0 <= -1.976380909090909)
         Predict: 0.0
        Else (feature 0 > -1.976380909090909)
         Predict: 3.0
       Else (feature 3 > 1.0884225728208126)
        If (feature 4 <= 1.9510708064802356)
         If (feature 3 <= 2.283530423945612)
          If (feature 0 <= -8.90539)
           Predict: 0.0
          Else (feature 0 > -8.90539)
           Predict: 2.0
         Else (feature 3 > 2.283530423945612)
          Predict: 0.0
        Else (feature 4 > 1.9510708064802356)
         If (feature 1 <= -10.088093363636364)
          If (feature 1 <= -17.407273636363634)
           Predict: 0.0
          Else (feature 1 > -17.407273636363634)
           Predict: 2.0
         Else (feature 1 > -10.088093363636364)
          Predict: 0.0
     Else (feature 4 > 2.2658035906051546)
      If (feature 3 <= 3.2305826019123103)
       If (feature 4 <= 4.527012376192168)
        If (feature 0 <= 3.5330349999999995)
         If (feature 0 <= -1.2744209090909089)
          If (feature 2 <= -1.8347482272727271)
           If (feature 5 <= 1.4557098485258626)
            If (feature 3 <= 1.5843984037196344)
             Predict: 4.0
            Else (feature 3 > 1.5843984037196344)
             Predict: 0.0
           Else (feature 5 > 1.4557098485258626)
            If (feature 0 <= -4.357466363636364)
             If (feature 0 <= -6.212517272727274)
              Predict: 0.0
             Else (feature 0 > -6.212517272727274)
              If (feature 5 <= 1.9145738226248412)
               If (feature 0 <= -4.988009090909092)
                If (feature 2 <= -2.198210545454545)
                 Predict: 0.0
                Else (feature 2 > -2.198210545454545)
                 Predict: 2.0
               Else (feature 0 > -4.988009090909092)
                Predict: 2.0
              Else (feature 5 > 1.9145738226248412)
               Predict: 2.0
            Else (feature 0 > -4.357466363636364)
             If (feature 1 <= -9.356527272727273)
              If (feature 4 <= 4.0067789684062625)
               If (feature 5 <= 2.0408593821519982)
                Predict: 0.0
               Else (feature 5 > 2.0408593821519982)
                Predict: 2.0
              Else (feature 4 > 4.0067789684062625)
               Predict: 4.0
             Else (feature 1 > -9.356527272727273)
              Predict: 0.0
          Else (feature 2 > -1.8347482272727271)
           If (feature 1 <= -3.8209812727272725)
            If (feature 1 <= -11.596521363636366)
             Predict: 0.0
            Else (feature 1 > -11.596521363636366)
             If (feature 5 <= 0.16873060661442005)
              Predict: 1.0
             Else (feature 5 > 0.16873060661442005)
              If (feature 5 <= 3.469288509552827)
               Predict: 2.0
              Else (feature 5 > 3.469288509552827)
               If (feature 4 <= 3.5166543239603483)
                Predict: 2.0
               Else (feature 4 > 3.5166543239603483)
                Predict: 0.0
           Else (feature 1 > -3.8209812727272725)
            If (feature 4 <= 3.5166543239603483)
             If (feature 2 <= 0.6682646818181818)
              Predict: 0.0
             Else (feature 2 > 0.6682646818181818)
              Predict: 2.0
            Else (feature 4 > 3.5166543239603483)
             Predict: 0.0
         Else (feature 0 > -1.2744209090909089)
          If (feature 2 <= 0.6682646818181818)
           If (feature 1 <= -3.8209812727272725)
            If (feature 5 <= 4.913251092952768)
             Predict: 2.0
            Else (feature 5 > 4.913251092952768)
             Predict: 0.0
           Else (feature 1 > -3.8209812727272725)
            If (feature 3 <= 2.696622839121874)
             Predict: 0.0
            Else (feature 3 > 2.696622839121874)
             If (feature 0 <= -0.1735843181818182)
              Predict: 0.0
             Else (feature 0 > -0.1735843181818182)
              If (feature 5 <= 0.7848413904200169)
               Predict: 4.0
              Else (feature 5 > 0.7848413904200169)
               Predict: 0.0
          Else (feature 2 > 0.6682646818181818)
           If (feature 1 <= -8.948937727272728)
            If (feature 0 <= 0.32806340909090914)
             Predict: 0.0
            Else (feature 0 > 0.32806340909090914)
             Predict: 2.0
           Else (feature 1 > -8.948937727272728)
            Predict: 0.0
        Else (feature 0 > 3.5330349999999995)
         If (feature 0 <= 4.459689318181818)
          If (feature 1 <= -13.810390454545457)
           Predict: 0.0
          Else (feature 1 > -13.810390454545457)
           If (feature 4 <= 2.891537202575433)
            Predict: 2.0
           Else (feature 4 > 2.891537202575433)
            Predict: 0.0
         Else (feature 0 > 4.459689318181818)
          Predict: 0.0
       Else (feature 4 > 4.527012376192168)
        If (feature 4 <= 8.132821942578682)
         If (feature 0 <= -3.502226363636364)
          If (feature 1 <= -11.596521363636366)
           If (feature 2 <= -3.569613363636363)
            Predict: 4.0
           Else (feature 2 > -3.569613363636363)
            If (feature 3 <= 2.9289256343028125)
             Predict: 2.0
            Else (feature 3 > 2.9289256343028125)
             Predict: 0.0
          Else (feature 1 > -11.596521363636366)
           If (feature 2 <= 0.6682646818181818)
            Predict: 0.0
           Else (feature 2 > 0.6682646818181818)
            If (feature 4 <= 6.684054463098245)
             Predict: 0.0
            Else (feature 4 > 6.684054463098245)
             Predict: 2.0
         Else (feature 0 > -3.502226363636364)
          If (feature 1 <= -3.8209812727272725)
           If (feature 0 <= 3.5330349999999995)
            If (feature 5 <= 3.1250203637838814)
             If (feature 2 <= -5.720776181818183)
              Predict: 2.0
             Else (feature 2 > -5.720776181818183)
              If (feature 2 <= 1.1333349545454543)
               If (feature 1 <= -5.109937500000001)
                If (feature 3 <= 1.7795641834853977)
                 Predict: 4.0
                Else (feature 3 > 1.7795641834853977)
                 If (feature 0 <= 0.9882181363636364)
                  If (feature 0 <= 0.5266320454545453)
                   If (feature 2 <= -1.2442670454545455)
                    If (feature 4 <= 5.398982323543355)
                     Predict: 0.0
                    Else (feature 4 > 5.398982323543355)
                     If (feature 5 <= 2.8353340431585474)
                      Predict: 4.0
                     Else (feature 5 > 2.8353340431585474)
                      If (feature 0 <= -1.4538298181818181)
                       If (feature 1 <= -9.49238863636364)
                        Predict: 0.0
                       Else (feature 1 > -9.49238863636364)
                        Predict: 4.0
                      Else (feature 0 > -1.4538298181818181)
                       Predict: 4.0
                   Else (feature 2 > -1.2442670454545455)
                    Predict: 0.0
                  Else (feature 0 > 0.5266320454545453)
                   Predict: 0.0
                 Else (feature 0 > 0.9882181363636364)
                  If (feature 4 <= 6.291886217849433)
                   Predict: 0.0
                  Else (feature 4 > 6.291886217849433)
                   If (feature 1 <= -5.750931181818182)
                    Predict: 4.0
                   Else (feature 1 > -5.750931181818182)
                    If (feature 3 <= 2.283530423945612)
                     Predict: 0.0
                    Else (feature 3 > 2.283530423945612)
                     If (feature 3 <= 2.696622839121874)
                      If (feature 3 <= 2.4887269112897967)
                       Predict: 4.0
                      Else (feature 3 > 2.4887269112897967)
                       Predict: 0.0
                     Else (feature 3 > 2.696622839121874)
                      Predict: 4.0
               Else (feature 1 > -5.109937500000001)
                If (feature 4 <= 5.019055660088064)
                 Predict: 0.0
                Else (feature 4 > 5.019055660088064)
                 If (feature 0 <= -3.065027181818182)
                  Predict: 2.0
                 Else (feature 0 > -3.065027181818182)
                  If (feature 4 <= 7.3665835405207885)
                   Predict: 4.0
                  Else (feature 4 > 7.3665835405207885)
                   Predict: 0.0
              Else (feature 2 > 1.1333349545454543)
               If (feature 3 <= 1.355530712657193)
                Predict: 2.0
               Else (feature 3 > 1.355530712657193)
                If (feature 5 <= 1.6130554927490437)
                 Predict: 0.0
                Else (feature 5 > 1.6130554927490437)
                 If (feature 0 <= -0.3791205454545455)
                  If (feature 4 <= 5.398982323543355)
                   Predict: 0.0
                  Else (feature 4 > 5.398982323543355)
                   Predict: 2.0
                 Else (feature 0 > -0.3791205454545455)
                  Predict: 4.0
            Else (feature 5 > 3.1250203637838814)
             If (feature 2 <= -1.8347482272727271)
              If (feature 1 <= -8.4067287012987)
               If (feature 3 <= 2.283530423945612)
                If (feature 0 <= 0.45695872727272724)
                 If (feature 3 <= 2.037322122633077)
                  If (feature 4 <= 5.019055660088064)
                   Predict: 4.0
                  Else (feature 4 > 5.019055660088064)
                   Predict: 0.0
                 Else (feature 3 > 2.037322122633077)
                  Predict: 2.0
                Else (feature 0 > 0.45695872727272724)
                 Predict: 4.0
               Else (feature 3 > 2.283530423945612)
                Predict: 0.0
              Else (feature 1 > -8.4067287012987)
               If (feature 5 <= 8.699793726565169)
                If (feature 1 <= -6.820416818181818)
                 If (feature 4 <= 7.033163268276285)
                  Predict: 4.0
                 Else (feature 4 > 7.033163268276285)
                  If (feature 3 <= 2.4887269112897967)
                   Predict: 0.0
                  Else (feature 3 > 2.4887269112897967)
                   Predict: 4.0
                Else (feature 1 > -6.820416818181818)
                 Predict: 4.0
               Else (feature 5 > 8.699793726565169)
                Predict: 0.0
             Else (feature 2 > -1.8347482272727271)
              If (feature 5 <= 3.8343025836297375)
               If (feature 1 <= -8.097179727272728)
                Predict: 2.0
               Else (feature 1 > -8.097179727272728)
                If (feature 3 <= 2.9289256343028125)
                 If (feature 3 <= 2.4887269112897967)
                  If (feature 0 <= -3.065027181818182)
                   Predict: 2.0
                  Else (feature 0 > -3.065027181818182)
                   Predict: 0.0
                 Else (feature 3 > 2.4887269112897967)
                  Predict: 0.0
                Else (feature 3 > 2.9289256343028125)
                 Predict: 4.0
              Else (feature 5 > 3.8343025836297375)
               If (feature 2 <= -0.9551237727272728)
                If (feature 5 <= 6.108758959317355)
                 Predict: 4.0
                Else (feature 5 > 6.108758959317355)
                 Predict: 0.0
               Else (feature 2 > -0.9551237727272728)
                Predict: 0.0
           Else (feature 0 > 3.5330349999999995)
            If (feature 4 <= 7.72195028414196)
             Predict: 0.0
            Else (feature 4 > 7.72195028414196)
             If (feature 1 <= -8.712045)
              Predict: 0.0
             Else (feature 1 > -8.712045)
              Predict: 4.0
          Else (feature 1 > -3.8209812727272725)
           If (feature 0 <= -3.065027181818182)
            Predict: 2.0
           Else (feature 0 > -3.065027181818182)
            If (feature 1 <= -2.2306882272727275)
             If (feature 3 <= 2.037322122633077)
              If (feature 5 <= 1.9145738226248412)
               Predict: 4.0
              Else (feature 5 > 1.9145738226248412)
               Predict: 0.0
             Else (feature 3 > 2.037322122633077)
              Predict: 0.0
            Else (feature 1 > -2.2306882272727275)
             Predict: 0.0
        Else (feature 4 > 8.132821942578682)
         If (feature 4 <= 8.97158739539847)
          If (feature 2 <= -1.2442670454545455)
           If (feature 1 <= -3.8209812727272725)
            If (feature 2 <= -5.720776181818183)
             Predict: 0.0
            Else (feature 2 > -5.720776181818183)
             If (feature 0 <= 0.43257299999999993)
              Predict: 4.0
             Else (feature 0 > 0.43257299999999993)
              If (feature 2 <= -2.810174181818182)
               Predict: 4.0
              Else (feature 2 > -2.810174181818182)
               Predict: 0.0
           Else (feature 1 > -3.8209812727272725)
            Predict: 0.0
          Else (feature 2 > -1.2442670454545455)
           If (feature 5 <= 1.9145738226248412)
            If (feature 2 <= 0.1387476818181818)
             Predict: 0.0
            Else (feature 2 > 0.1387476818181818)
             If (feature 0 <= -0.7518725909090909)
              Predict: 0.0
             Else (feature 0 > -0.7518725909090909)
              Predict: 4.0
           Else (feature 5 > 1.9145738226248412)
            Predict: 0.0
         Else (feature 4 > 8.97158739539847)
          Predict: 0.0
      Else (feature 3 > 3.2305826019123103)
       If (feature 1 <= -10.088093363636364)
        If (feature 3 <= 5.42131601555171)
         If (feature 0 <= 2.0089040454545453)
          If (feature 5 <= 4.262646949929151)
           If (feature 0 <= -8.90539)
            Predict: 0.0
           Else (feature 0 > -8.90539)
            If (feature 4 <= 4.527012376192168)
             If (feature 0 <= -0.7518725909090909)
              If (feature 5 <= 1.2559600735949519)
               Predict: 0.0
              Else (feature 5 > 1.2559600735949519)
               Predict: 2.0
             Else (feature 0 > -0.7518725909090909)
              Predict: 0.0
            Else (feature 4 > 4.527012376192168)
             If (feature 4 <= 6.684054463098245)
              Predict: 0.0
             Else (feature 4 > 6.684054463098245)
              If (feature 0 <= 0.32806340909090914)
               If (feature 4 <= 7.3665835405207885)
                If (feature 3 <= 4.596493775946534)
                 Predict: 2.0
                Else (feature 3 > 4.596493775946534)
                 Predict: 0.0
               Else (feature 4 > 7.3665835405207885)
                Predict: 0.0
              Else (feature 0 > 0.32806340909090914)
               Predict: 2.0
          Else (feature 5 > 4.262646949929151)
           Predict: 0.0
         Else (feature 0 > 2.0089040454545453)
          Predict: 0.0
        Else (feature 3 > 5.42131601555171)
         Predict: 0.0
       Else (feature 1 > -10.088093363636364)
        If (feature 5 <= 1.0680362943269028)
         If (feature 4 <= 5.830630767197693)

*** WARNING: skipped 325736 bytes of output ***

                Predict: 2.0
             Else (feature 4 > 5.398982323543355)
              If (feature 0 <= -2.5093826818181824)
               Predict: 4.0
              Else (feature 0 > -2.5093826818181824)
               Predict: 2.0
            Else (feature 1 > -7.698300909090908)
             If (feature 2 <= -1.073569090909091)
              If (feature 3 <= 2.696622839121874)
               If (feature 1 <= -7.295937636363637)
                If (feature 3 <= 1.7795641834853977)
                 Predict: 4.0
                Else (feature 3 > 1.7795641834853977)
                 Predict: 0.0
               Else (feature 1 > -7.295937636363637)
                If (feature 4 <= 4.527012376192168)
                 Predict: 2.0
                Else (feature 4 > 4.527012376192168)
                 Predict: 0.0
              Else (feature 3 > 2.696622839121874)
               Predict: 0.0
             Else (feature 2 > -1.073569090909091)
              Predict: 2.0
         Else (feature 1 > -5.750931181818182)
          If (feature 3 <= 5.42131601555171)
           If (feature 2 <= 6.177680772727272)
            If (feature 1 <= -2.2306882272727275)
             If (feature 3 <= 1.7795641834853977)
              Predict: 4.0
             Else (feature 3 > 1.7795641834853977)
              If (feature 5 <= 1.2559600735949519)
               If (feature 4 <= 2.891537202575433)
                Predict: 2.0
               Else (feature 4 > 2.891537202575433)
                Predict: 0.0
              Else (feature 5 > 1.2559600735949519)
               If (feature 0 <= 2.0089040454545453)
                Predict: 0.0
               Else (feature 0 > 2.0089040454545453)
                If (feature 4 <= 2.891537202575433)
                 Predict: 0.0
                Else (feature 4 > 2.891537202575433)
                 Predict: 4.0
            Else (feature 1 > -2.2306882272727275)
             If (feature 4 <= 3.5166543239603483)
              If (feature 0 <= 0.9882181363636364)
               Predict: 0.0
              Else (feature 0 > 0.9882181363636364)
               If (feature 5 <= 0.7848413904200169)
                Predict: 4.0
               Else (feature 5 > 0.7848413904200169)
                Predict: 0.0
             Else (feature 4 > 3.5166543239603483)
              Predict: 0.0
           Else (feature 2 > 6.177680772727272)
            Predict: 2.0
          Else (feature 3 > 5.42131601555171)
           If (feature 4 <= 5.019055660088064)
            Predict: 0.0
           Else (feature 4 > 5.019055660088064)
            If (feature 3 <= 7.291873594931593)
             If (feature 1 <= -5.109937500000001)
              Predict: 0.0
             Else (feature 1 > -5.109937500000001)
              If (feature 1 <= -2.2306882272727275)
               Predict: 4.0
              Else (feature 1 > -2.2306882272727275)
               Predict: 0.0
            Else (feature 3 > 7.291873594931593)
             Predict: 0.0
       Else (feature 4 > 5.830630767197693)
        If (feature 0 <= -3.065027181818182)
         If (feature 4 <= 7.3665835405207885)
          If (feature 5 <= 1.4557098485258626)
           If (feature 3 <= 3.5937950153207483)
            If (feature 4 <= 6.291886217849433)
             If (feature 0 <= -3.502226363636364)
              Predict: 0.0
             Else (feature 0 > -3.502226363636364)
              Predict: 4.0
            Else (feature 4 > 6.291886217849433)
             Predict: 0.0
           Else (feature 3 > 3.5937950153207483)
            If (feature 2 <= 0.24674104545454545)
             Predict: 4.0
            Else (feature 2 > 0.24674104545454545)
             If (feature 3 <= 5.42131601555171)
              Predict: 4.0
             Else (feature 3 > 5.42131601555171)
              Predict: 0.0
          Else (feature 5 > 1.4557098485258626)
           If (feature 2 <= 0.1387476818181818)
            Predict: 0.0
           Else (feature 2 > 0.1387476818181818)
            If (feature 3 <= 1.0884225728208126)
             Predict: 2.0
            Else (feature 3 > 1.0884225728208126)
             Predict: 4.0
         Else (feature 4 > 7.3665835405207885)
          If (feature 3 <= 4.092746328718364)
           Predict: 0.0
          Else (feature 3 > 4.092746328718364)
           Predict: 4.0
        Else (feature 0 > -3.065027181818182)
         If (feature 1 <= -9.433167272727271)
          If (feature 2 <= -3.569613363636363)
           If (feature 3 <= 2.037322122633077)
            Predict: 4.0
           Else (feature 3 > 2.037322122633077)
            Predict: 2.0
          Else (feature 2 > -3.569613363636363)
           If (feature 0 <= -1.976380909090909)
            If (feature 0 <= -2.5093826818181824)
             Predict: 4.0
            Else (feature 0 > -2.5093826818181824)
             Predict: 2.0
           Else (feature 0 > -1.976380909090909)
            Predict: 0.0
         Else (feature 1 > -9.433167272727271)
          If (feature 3 <= 8.670695907958262)
           If (feature 2 <= 1.1333349545454543)
            If (feature 1 <= -1.1942972727272725)
             If (feature 4 <= 8.97158739539847)
              If (feature 2 <= -0.6624949545454546)
               If (feature 0 <= 7.344164590909091)
                If (feature 2 <= -5.720776181818183)
                 Predict: 0.0
                Else (feature 2 > -5.720776181818183)
                 If (feature 1 <= -2.2306882272727275)
                  If (feature 1 <= -5.750931181818182)
                   Predict: 4.0
                  Else (feature 1 > -5.750931181818182)
                   If (feature 3 <= 2.9289256343028125)
                    If (feature 0 <= 1.2895552272727273)
                     Predict: 4.0
                    Else (feature 0 > 1.2895552272727273)
                     Predict: 0.0
                   Else (feature 3 > 2.9289256343028125)
                    Predict: 4.0
                 Else (feature 1 > -2.2306882272727275)
                  If (feature 5 <= 2.179859408203087)
                   Predict: 4.0
                  Else (feature 5 > 2.179859408203087)
                   Predict: 0.0
               Else (feature 0 > 7.344164590909091)
                If (feature 5 <= 1.7614806708061252)
                 Predict: 4.0
                Else (feature 5 > 1.7614806708061252)
                 Predict: 0.0
              Else (feature 2 > -0.6624949545454546)
               If (feature 4 <= 7.033163268276285)
                If (feature 2 <= -0.5126976818181816)
                 Predict: 4.0
                Else (feature 2 > -0.5126976818181816)
                 If (feature 1 <= -3.8209812727272725)
                  If (feature 2 <= 0.29899645454545454)
                   If (feature 5 <= 1.7614806708061252)
                    Predict: 0.0
                   Else (feature 5 > 1.7614806708061252)
                    Predict: 4.0
                  Else (feature 2 > 0.29899645454545454)
                   Predict: 4.0
                 Else (feature 1 > -3.8209812727272725)
                  Predict: 0.0
               Else (feature 4 > 7.033163268276285)
                If (feature 5 <= 2.0408593821519982)
                 If (feature 1 <= -5.109937500000001)
                  If (feature 3 <= 1.5843984037196344)
                   Predict: 0.0
                  Else (feature 3 > 1.5843984037196344)
                   Predict: 4.0
                 Else (feature 1 > -5.109937500000001)
                  Predict: 0.0
                Else (feature 5 > 2.0408593821519982)
                 If (feature 0 <= 3.5330349999999995)
                  If (feature 1 <= -5.750931181818182)
                   If (feature 3 <= 2.037322122633077)
                    Predict: 0.0
                   Else (feature 3 > 2.037322122633077)
                    Predict: 4.0
                  Else (feature 1 > -5.750931181818182)
                   Predict: 0.0
                 Else (feature 0 > 3.5330349999999995)
                  If (feature 5 <= 2.179859408203087)
                   Predict: 4.0
                  Else (feature 5 > 2.179859408203087)
                   Predict: 0.0
             Else (feature 4 > 8.97158739539847)
              Predict: 0.0
            Else (feature 1 > -1.1942972727272725)
             Predict: 0.0
           Else (feature 2 > 1.1333349545454543)
            If (feature 1 <= -5.750931181818182)
             If (feature 0 <= 0.9882181363636364)
              If (feature 5 <= 1.7614806708061252)
               Predict: 0.0
              Else (feature 5 > 1.7614806708061252)
               Predict: 2.0
             Else (feature 0 > 0.9882181363636364)
              Predict: 4.0
            Else (feature 1 > -5.750931181818182)
             Predict: 0.0
          Else (feature 3 > 8.670695907958262)
           Predict: 0.0
      Else (feature 5 > 2.600144428803959)
       If (feature 3 <= 3.2305826019123103)
        If (feature 0 <= 2.0089040454545453)
         If (feature 1 <= -7.698300909090908)
          If (feature 5 <= 6.108758959317355)
           If (feature 1 <= -11.596521363636366)
            If (feature 0 <= -8.90539)
             Predict: 0.0
            Else (feature 0 > -8.90539)
             If (feature 0 <= -1.976380909090909)
              If (feature 3 <= 2.9289256343028125)
               If (feature 5 <= 3.8343025836297375)
                Predict: 4.0
               Else (feature 5 > 3.8343025836297375)
                Predict: 2.0
              Else (feature 3 > 2.9289256343028125)
               Predict: 0.0
             Else (feature 0 > -1.976380909090909)
              If (feature 4 <= 7.033163268276285)
               Predict: 2.0
              Else (feature 4 > 7.033163268276285)
               Predict: 0.0
           Else (feature 1 > -11.596521363636366)
            If (feature 2 <= -2.4757429090909095)
             If (feature 3 <= 2.037322122633077)
              Predict: 4.0
             Else (feature 3 > 2.037322122633077)
              If (feature 4 <= 6.291886217849433)
               Predict: 2.0
              Else (feature 4 > 6.291886217849433)
               Predict: 0.0
            Else (feature 2 > -2.4757429090909095)
             If (feature 4 <= 6.684054463098245)
              Predict: 2.0
             Else (feature 4 > 6.684054463098245)
              If (feature 1 <= -10.088093363636364)
               Predict: 2.0
              Else (feature 1 > -10.088093363636364)
               If (feature 0 <= -4.214637727272727)
                Predict: 2.0
               Else (feature 0 > -4.214637727272727)
                Predict: 0.0
          Else (feature 5 > 6.108758959317355)
           If (feature 1 <= -9.276403181818182)
            Predict: 0.0
           Else (feature 1 > -9.276403181818182)
            If (feature 2 <= -3.569613363636363)
             Predict: 4.0
            Else (feature 2 > -3.569613363636363)
             Predict: 0.0
         Else (feature 1 > -7.698300909090908)
          If (feature 4 <= 6.684054463098245)
           If (feature 4 <= 3.5166543239603483)
            If (feature 1 <= -5.109937500000001)
             Predict: 2.0
            Else (feature 1 > -5.109937500000001)
             If (feature 2 <= 1.1333349545454543)
              Predict: 0.0
             Else (feature 2 > 1.1333349545454543)
              Predict: 2.0
           Else (feature 4 > 3.5166543239603483)
            If (feature 3 <= 1.7795641834853977)
             If (feature 5 <= 3.469288509552827)
              If (feature 0 <= -3.065027181818182)
               If (feature 1 <= -3.8209812727272725)
                Predict: 2.0
               Else (feature 1 > -3.8209812727272725)
                Predict: 0.0
              Else (feature 0 > -3.065027181818182)
               Predict: 4.0
             Else (feature 5 > 3.469288509552827)
              Predict: 0.0
            Else (feature 3 > 1.7795641834853977)
             If (feature 1 <= -7.295937636363637)
              Predict: 2.0
             Else (feature 1 > -7.295937636363637)
              If (feature 4 <= 4.0067789684062625)
               If (feature 0 <= -1.105464818181818)
                Predict: 0.0
               Else (feature 0 > -1.105464818181818)
                Predict: 2.0
              Else (feature 4 > 4.0067789684062625)
               Predict: 0.0
          Else (feature 4 > 6.684054463098245)
           If (feature 1 <= -3.8209812727272725)
            If (feature 5 <= 8.699793726565169)
             If (feature 3 <= 1.5843984037196344)
              Predict: 0.0
             Else (feature 3 > 1.5843984037196344)
              If (feature 2 <= -1.073569090909091)
               If (feature 5 <= 4.262646949929151)
                Predict: 4.0
               Else (feature 5 > 4.262646949929151)
                If (feature 0 <= 0.9882181363636364)
                 If (feature 2 <= -2.198210545454545)
                  Predict: 4.0
                 Else (feature 2 > -2.198210545454545)
                  If (feature 0 <= 0.43257299999999993)
                   Predict: 4.0
                  Else (feature 0 > 0.43257299999999993)
                   Predict: 0.0
                Else (feature 0 > 0.9882181363636364)
                 Predict: 4.0
              Else (feature 2 > -1.073569090909091)
               If (feature 3 <= 2.4887269112897967)
                Predict: 2.0
               Else (feature 3 > 2.4887269112897967)
                Predict: 0.0
            Else (feature 5 > 8.699793726565169)
             Predict: 0.0
           Else (feature 1 > -3.8209812727272725)
            If (feature 5 <= 3.469288509552827)
             Predict: 2.0
            Else (feature 5 > 3.469288509552827)
             Predict: 0.0
        Else (feature 0 > 2.0089040454545453)
         If (feature 5 <= 2.8353340431585474)
          If (feature 2 <= -5.720776181818183)
           Predict: 2.0
          Else (feature 2 > -5.720776181818183)
           Predict: 0.0
         Else (feature 5 > 2.8353340431585474)
          If (feature 4 <= 4.0067789684062625)
           If (feature 3 <= 2.283530423945612)
            If (feature 1 <= -13.810390454545457)
             Predict: 0.0
            Else (feature 1 > -13.810390454545457)
             If (feature 2 <= -1.2442670454545455)
              Predict: 2.0
             Else (feature 2 > -1.2442670454545455)
              Predict: 0.0
           Else (feature 3 > 2.283530423945612)
            Predict: 0.0
          Else (feature 4 > 4.0067789684062625)
           If (feature 5 <= 3.1250203637838814)
            If (feature 3 <= 2.9289256343028125)
             Predict: 0.0
            Else (feature 3 > 2.9289256343028125)
             If (feature 0 <= 2.8563333333333336)
              Predict: 4.0
             Else (feature 0 > 2.8563333333333336)
              Predict: 0.0
           Else (feature 5 > 3.1250203637838814)
            Predict: 0.0
       Else (feature 3 > 3.2305826019123103)
        If (feature 3 <= 4.596493775946534)
         If (feature 1 <= -8.097179727272728)
          If (feature 5 <= 3.469288509552827)
           If (feature 3 <= 3.5937950153207483)
            If (feature 5 <= 2.8353340431585474)
             Predict: 0.0
            Else (feature 5 > 2.8353340431585474)
             If (feature 0 <= 2.0089040454545453)
              If (feature 4 <= 5.398982323543355)
               If (feature 0 <= -8.90539)
                Predict: 0.0
               Else (feature 0 > -8.90539)
                Predict: 2.0
              Else (feature 4 > 5.398982323543355)
               Predict: 2.0
             Else (feature 0 > 2.0089040454545453)
              Predict: 0.0
           Else (feature 3 > 3.5937950153207483)
            If (feature 2 <= -0.6624949545454546)
             Predict: 0.0
            Else (feature 2 > -0.6624949545454546)
             If (feature 0 <= 2.0089040454545453)
              If (feature 4 <= 7.3665835405207885)
               If (feature 5 <= 3.1250203637838814)
                Predict: 2.0
               Else (feature 5 > 3.1250203637838814)
                Predict: 0.0
              Else (feature 4 > 7.3665835405207885)
               Predict: 0.0
             Else (feature 0 > 2.0089040454545453)
              Predict: 0.0
          Else (feature 5 > 3.469288509552827)
           If (feature 3 <= 4.092746328718364)
            Predict: 0.0
           Else (feature 3 > 4.092746328718364)
            If (feature 4 <= 7.72195028414196)
             Predict: 0.0
            Else (feature 4 > 7.72195028414196)
             If (feature 1 <= -11.596521363636366)
              If (feature 5 <= 4.262646949929151)
               Predict: 2.0
              Else (feature 5 > 4.262646949929151)
               Predict: 0.0
             Else (feature 1 > -11.596521363636366)
              If (feature 1 <= -8.4067287012987)
               Predict: 0.0
              Else (feature 1 > -8.4067287012987)
               If (feature 0 <= 1.2895552272727273)
                Predict: 0.0
               Else (feature 0 > 1.2895552272727273)
                Predict: 4.0
         Else (feature 1 > -8.097179727272728)
          If (feature 1 <= -3.8209812727272725)
           If (feature 2 <= -1.5194759545454544)
            If (feature 4 <= 5.830630767197693)
             Predict: 0.0
            Else (feature 4 > 5.830630767197693)
             If (feature 5 <= 6.108758959317355)
              If (feature 2 <= -5.720776181818183)
               Predict: 0.0
              Else (feature 2 > -5.720776181818183)
               If (feature 0 <= -0.7518725909090909)
                Predict: 0.0
               Else (feature 0 > -0.7518725909090909)
                Predict: 4.0
             Else (feature 5 > 6.108758959317355)
              Predict: 0.0
           Else (feature 2 > -1.5194759545454544)
            If (feature 2 <= -0.18174913636363638)
             If (feature 1 <= -5.750931181818182)
              Predict: 0.0
             Else (feature 1 > -5.750931181818182)
              If (feature 0 <= -7.121753363636364)
               Predict: 0.0
              Else (feature 0 > -7.121753363636364)
               Predict: 4.0
            Else (feature 2 > -0.18174913636363638)
             If (feature 0 <= 2.0089040454545453)
              If (feature 2 <= 1.1333349545454543)
               If (feature 0 <= -1.976380909090909)
                If (feature 0 <= -4.165866818181819)
                 Predict: 2.0
                Else (feature 0 > -4.165866818181819)
                 Predict: 0.0
               Else (feature 0 > -1.976380909090909)
                Predict: 2.0
              Else (feature 2 > 1.1333349545454543)
               Predict: 0.0
             Else (feature 0 > 2.0089040454545453)
              If (feature 0 <= 4.459689318181818)
               Predict: 4.0
              Else (feature 0 > 4.459689318181818)
               Predict: 0.0
          Else (feature 1 > -3.8209812727272725)
           If (feature 2 <= 0.6682646818181818)
            Predict: 0.0
           Else (feature 2 > 0.6682646818181818)
            If (feature 4 <= 3.5166543239603483)
             Predict: 2.0
            Else (feature 4 > 3.5166543239603483)
             Predict: 0.0
        Else (feature 3 > 4.596493775946534)
         If (feature 4 <= 6.291886217849433)
          If (feature 2 <= -0.6624949545454546)
           If (feature 5 <= 2.8353340431585474)
            If (feature 0 <= 0.43257299999999993)
             Predict: 0.0
            Else (feature 0 > 0.43257299999999993)
             If (feature 3 <= 8.670695907958262)
              If (feature 3 <= 6.490709433033732)
               Predict: 4.0
              Else (feature 3 > 6.490709433033732)
               If (feature 3 <= 7.291873594931593)
                Predict: 0.0
               Else (feature 3 > 7.291873594931593)
                Predict: 4.0
             Else (feature 3 > 8.670695907958262)
              Predict: 0.0
           Else (feature 5 > 2.8353340431585474)
            If (feature 1 <= -6.820416818181818)
             If (feature 1 <= -7.295937636363637)
              Predict: 0.0
             Else (feature 1 > -7.295937636363637)
              If (feature 2 <= -1.8347482272727271)
               Predict: 0.0
              Else (feature 2 > -1.8347482272727271)
               If (feature 2 <= -1.073569090909091)
                Predict: 2.0
               Else (feature 2 > -1.073569090909091)
                Predict: 0.0
            Else (feature 1 > -6.820416818181818)
             Predict: 0.0
          Else (feature 2 > -0.6624949545454546)
           If (feature 1 <= -8.948937727272728)
            Predict: 0.0
           Else (feature 1 > -8.948937727272728)
            If (feature 3 <= 5.42131601555171)
             If (feature 0 <= 0.32806340909090914)
              If (feature 1 <= -6.2386456818181815)
               If (feature 0 <= -3.065027181818182)
                Predict: 0.0
               Else (feature 0 > -3.065027181818182)
                If (feature 5 <= 4.262646949929151)
                 Predict: 2.0
                Else (feature 5 > 4.262646949929151)
                 Predict: 0.0
              Else (feature 1 > -6.2386456818181815)
               Predict: 2.0
             Else (feature 0 > 0.32806340909090914)
              Predict: 0.0
            Else (feature 3 > 5.42131601555171)
             If (feature 2 <= -0.5126976818181816)
              If (feature 3 <= 5.87583214563006)
               If (feature 4 <= 5.019055660088064)
                Predict: 0.0
               Else (feature 4 > 5.019055660088064)
                Predict: 2.0
              Else (feature 3 > 5.87583214563006)
               Predict: 0.0
             Else (feature 2 > -0.5126976818181816)
              Predict: 0.0
         Else (feature 4 > 6.291886217849433)
          If (feature 5 <= 2.8353340431585474)
           If (feature 4 <= 7.72195028414196)
            If (feature 0 <= 5.969858363636364)
             If (feature 0 <= -7.121753363636364)
              Predict: 0.0
             Else (feature 0 > -7.121753363636364)
              Predict: 4.0
            Else (feature 0 > 5.969858363636364)
             Predict: 0.0
           Else (feature 4 > 7.72195028414196)
            Predict: 0.0
          Else (feature 5 > 2.8353340431585474)
           If (feature 3 <= 5.42131601555171)
            If (feature 2 <= -1.073569090909091)
             If (feature 5 <= 4.913251092952768)
              If (feature 0 <= 2.8563333333333336)
               If (feature 4 <= 7.3665835405207885)
                Predict: 0.0
               Else (feature 4 > 7.3665835405207885)
                If (feature 0 <= -5.695193636363635)
                 Predict: 2.0
                Else (feature 0 > -5.695193636363635)
                 Predict: 4.0
              Else (feature 0 > 2.8563333333333336)
               Predict: 0.0
             Else (feature 5 > 4.913251092952768)
              Predict: 0.0
            Else (feature 2 > -1.073569090909091)
             If (feature 5 <= 4.262646949929151)
              If (feature 2 <= -0.18174913636363638)
               If (feature 5 <= 3.8343025836297375)
                Predict: 0.0
               Else (feature 5 > 3.8343025836297375)
                Predict: 2.0
              Else (feature 2 > -0.18174913636363638)
               Predict: 0.0
             Else (feature 5 > 4.262646949929151)
              Predict: 0.0
           Else (feature 3 > 5.42131601555171)
            If (feature 1 <= -8.948937727272728)
             Predict: 0.0
            Else (feature 1 > -8.948937727272728)
             If (feature 1 <= -8.712045)
              If (feature 3 <= 5.87583214563006)
               Predict: 4.0
              Else (feature 3 > 5.87583214563006)
               Predict: 0.0
             Else (feature 1 > -8.712045)
              If (feature 2 <= -0.6624949545454546)
               If (feature 5 <= 3.1250203637838814)
                If (feature 3 <= 8.670695907958262)
                 If (feature 3 <= 6.490709433033732)
                  Predict: 4.0
                 Else (feature 3 > 6.490709433033732)
                  If (feature 4 <= 7.033163268276285)
                   Predict: 0.0
                  Else (feature 4 > 7.033163268276285)
                   If (feature 2 <= -1.0073777272727273)
                    If (feature 1 <= -5.109937500000001)
                     Predict: 4.0
                    Else (feature 1 > -5.109937500000001)
                     Predict: 0.0
                   Else (feature 2 > -1.0073777272727273)
                    Predict: 0.0
                Else (feature 3 > 8.670695907958262)
                 Predict: 0.0
               Else (feature 5 > 3.1250203637838814)
                Predict: 0.0
              Else (feature 2 > -0.6624949545454546)
               Predict: 0.0

import org.apache.spark.ml.classification.RandomForestClassificationModel
rfModel: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6

Save and Load later to Serve Predictions

When you are satisfied with the model you have trained/fitted then you can save it to a file and load it again from another low-latency prediciton-serving system to serve predictions.

model
res24: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.PipelineModel

Save the model to a file in stable storage.

model.write.overwrite().save("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")

Load the model, typically from a prediction or model serving layer/system.

val same_rfModelFromSavedFile = PipelineModel.load("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
same_rfModelFromSavedFile: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
same_rfModelFromSavedFile
res26: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c

Suppose new data is coming at us and we need to serve our predicitons of the activities

Note: We only need accelerometer readings so prediciton pipeline can be simplified to only take in the needed features for prediction. For simplicity let's assume the test data is representative of the new data (previously unseen) that is coming at us for serving our activity predicitons.

val newDataForPrediction = testData.sample(0.0004,12348L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
meanX meanY meanZ stddevX stddevY stddevZ
-1.9955399999999999 -9.258980909090907 1.0410163636363636 1.0549255357138732 0.7680301427703919 1.8853035398286844

Here is another new data coming at you... you can predict the current activity being done by simply transforming the loaded model (estimator).

val newDataForPrediction = testData.sample(0.0004,1234L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
meanX meanY meanZ stddevX stddevY stddevZ
0.42212218181818173 -0.7135524545454545 9.440134545454546 8.409472091286836e-2 9.073949633706861e-2 9.389382640736067e-2
display(same_rfModelFromSavedFile.transform(newDataForPrediction).select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction"))
meanX meanY meanZ stddevX stddevY stddevZ prediction
0.42212218181818173 -0.7135524545454545 9.440134545454546 8.409472091286836e-2 9.073949633706861e-2 9.389382640736067e-2 3.0

Some minor post-processing to connect prediction label to the activity string.

Typically one prediction is just a small part of larger business or science use-case.

activityLabelDF.show
+--------+-----+
|activity|label|
+--------+-----+
| Jogging|  0.0|
| Jumping|  4.0|
|Standing|  1.0|
| Walking|  2.0|
| Sitting|  3.0|
+--------+-----+
display(same_rfModelFromSavedFile
        .transform(newDataForPrediction)
        .select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction")
        .join(activityLabelDF,$"prediction"===$"label")
        .drop("prediction","label")
        .withColumnRenamed("activity","Your current activity is likely to be")
       )
meanX meanY meanZ stddevX stddevY stddevZ Your current activity is likely to be
0.42212218181818173 -0.7135524545454545 9.440134545454546 8.409472091286836e-2 9.073949633706861e-2 9.389382640736067e-2 Sitting

An activity-based music-recommendation system - a diatribe:

For example, you may want to correlate historical music-listening habits for a user with their historical physical activities and then recommend music to them using a subsequent recommender system pipeline that uses activity prediciton as one of its input.

NOTE: One has to be legally compliant for using the data in such manner with client consent, depending on the jurisdiction of your operations - GDPR applies for EU operations, for example --- this is coming attraction!

display(same_rfModelFromSavedFile.transform(testData.sample(0.01))) // note more can be done with such data, time of day can be informative for other decision problems
  1. Download and Load Data

The following anonymized dataset is used with kind permission of Amira Lakhal.

wget http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
--2020-11-13 09:48:32--  http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
Resolving lamastex.org (lamastex.org)... 166.62.28.100
Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 931349 (910K) [text/csv]
Saving to: ‘dataTraining.csv’

     0K .......... .......... .......... .......... ..........  5%  140K 6s
    50K .......... .......... .......... .......... .......... 10%  282K 4s
   100K .......... .......... .......... .......... .......... 16%  288K 4s
   150K .......... .......... .......... .......... .......... 21% 14.8M 3s
   200K .......... .......... .......... .......... .......... 27% 33.2M 2s
   250K .......... .......... .......... .......... .......... 32%  287K 2s
   300K .......... .......... .......... .......... .......... 38% 35.0M 1s
   350K .......... .......... .......... .......... .......... 43% 35.7M 1s
   400K .......... .......... .......... .......... .......... 49% 48.3M 1s
   450K .......... .......... .......... .......... .......... 54%  293K 1s
   500K .......... .......... .......... .......... .......... 60% 20.8M 1s
   550K .......... .......... .......... .......... .......... 65% 44.5M 1s
   600K .......... .......... .......... .......... .......... 71% 39.1M 0s
   650K .......... .......... .......... .......... .......... 76% 44.6M 0s
   700K .......... .......... .......... .......... .......... 82% 41.9M 0s
   750K .......... .......... .......... .......... .......... 87% 42.0M 0s
   800K .......... .......... .......... .......... .......... 93% 42.4M 0s
   850K .......... .......... .......... .......... .......... 98%  297K 0s
   900K .........                                             100% 35.8M=1.2s

2020-11-13 09:48:33 (734 KB/s) - ‘dataTraining.csv’ saved [931349/931349]
pwd && ls
/databricks/driver
conf
dataTraining.csv
derby.log
eventlogs
ganglia
logs
//dbutils.fs.mkdirs("dbfs:///datasets/sds/ActivityRecognition") // make directory if needed
dbutils.fs.mv("file:///databricks/driver/dataTraining.csv","dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
res25: Boolean = true
display(dbutils.fs.ls("dbfs:///datasets/sds/ActivityRecognition"))
path name size
dbfs:/datasets/sds/ActivityRecognition/dataTraining.csv dataTraining.csv 931349.0
dbutils.fs.head("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
[Truncated to first 65536 bytes]
res27: String =
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
"user_001","Jumping",1446047227865,"3.44943","-1.68549","2.29862"
"user_001","Jumping",1446047227930,"1.87829","-1.91542","0.880768"
"user_001","Jumping",1446047227995,"1.57173","-5.86241","-3.75599"
"user_001","Jumping",1446047228059,"3.41111","-17.93331","0.535886"
"user_001","Jumping",1446047228123,"3.18118","-19.58108","5.74745"
"user_001","Jumping",1446047228189,"7.85626","-19.2362","0.804128"
"user_001","Jumping",1446047228253,"1.26517","-8.85139","2.18366"
"user_001","Jumping",1446047228318,"0.077239","1.15021","1.53221"
"user_001","Jumping",1446047228383,"0.230521","2.0699","-1.41845"
"user_001","Jumping",1446047228447,"0.652044","-0.497565","1.76214"
"user_001","Jumping",1446047228512,"1.53341","-0.305964","1.41725"
"user_001","Jumping",1446047228578,"-1.07237","-1.95374","0.191003"
"user_001","Jumping",1446047228642,"2.75966","-13.75639","0.191003"
"user_001","Jumping",1446047228707,"7.43474","-19.58108","2.95007"
"user_001","Jumping",1446047228771,"5.63368","-19.58108","5.59417"
"user_001","Jumping",1446047228836,"5.02056","-11.72542","-1.38013"
"user_001","Jumping",1446047228900,"-2.10702","0.575403","1.07237"
"user_001","Jumping",1446047228966,"-1.30229","2.2615","-1.26517"
"user_001","Jumping",1446047229030,"1.68669","-0.957409","1.57053"
"user_001","Jumping",1446047229095,"2.60638","-0.229323","2.14534"
"user_001","Jumping",1446047229159,"1.30349","-0.152682","0.497565"
"user_001","Jumping",1446047229224,"1.64837","-8.12331","1.60885"
"user_001","Jumping",1446047229290,"0.15388","-18.46979","-1.03525"
"user_001","Jumping",1446047229354,"4.98224","-19.58108","0.995729"
"user_001","Jumping",1446047229419,"5.17384","-19.2362","0.612526"
"user_001","Jumping",1446047229483,"2.03158","-9.11964","1.34061"
"user_001","Jumping",1446047229549,"-1.95374","-1.03405","0.574206"
"user_001","Jumping",1446047229612,"0.1922","1.30349","-1.03525"
"user_001","Jumping",1446047229678,"1.64837","-0.727487","-0.307161"
"user_001","Jumping",1446047229743,"2.56806","-1.03405","0.267643"
"user_001","Jumping",1446047229806,"1.41845","-0.305964","0.727487"
"user_001","Jumping",1446047229872,"1.03525","-6.82042","0.957409"
"user_001","Jumping",1446047229936,"3.94759","-19.31284","-1.22685"
"user_001","Jumping",1446047230001,"6.13185","-19.58108","2.72014"
"user_001","Jumping",1446047230066,"7.89458","-16.9753","0.229323"
"user_001","Jumping",1446047230131,"2.6447","-3.75479","0.114362"
"user_001","Jumping",1446047230195,"-2.41358","1.91661","-0.000599"
"user_001","Jumping",1446047230260,"-1.95374","-0.114362","-0.268841"
"user_001","Jumping",1446047230325,"-0.229323","-1.95374","2.75846"
"user_001","Jumping",1446047230390,"2.68302","0.268841","0.535886"
"user_001","Jumping",1446047230455,"1.15021","-1.41725","0.880768"
"user_001","Jumping",1446047230519,"2.98958","-11.53382","-0.920286"
"user_001","Jumping",1446047230584,"8.00954","-19.58108","-0.1922"
"user_001","Jumping",1446047230649,"7.31978","-19.58108","2.52854"
"user_001","Jumping",1446047230713,"5.44208","-13.56479","-0.498763"
"user_001","Jumping",1446047230779,"0.728685","-0.420925","1.68549"
"user_001","Jumping",1446047230842,"1.18853","1.41845","-2.22318"
"user_001","Jumping",1446047230907,"0.15388","-1.80046","2.03038"
"user_001","Jumping",1446047230972,"-0.037722","1.07357","-0.958607"
"user_001","Jumping",1446047231038,"0.230521","0.728685","-0.690364"
"user_001","Jumping",1446047231102,"0.805325","-1.14901","1.53221"
"user_001","Jumping",1446047231167,"5.17384","-9.15796","-2.91294"
"user_001","Jumping",1446047231231,"8.00954","-19.54276","1.49389"
"user_001","Jumping",1446047231296,"11.61165","-19.58108","4.25296"
"user_001","Jumping",1446047231361,"7.89458","-18.35483","3.71647"
"user_001","Jumping",1446047231426,"0.537083","-3.21831","0.420925"
"user_001","Jumping",1446047231490,"-1.91542","4.10087","-2.6447"
"user_001","Jumping",1446047231555,"-0.919089","-0.382604","0.114362"
"user_001","Jumping",1446047231620,"0.613724","-0.842448","1.57053"
"user_001","Jumping",1446047231684,"1.38013","-0.459245","0.305964"
"user_001","Jumping",1446047231749,"2.29982","-1.49389","-1.26517"
"user_001","Jumping",1446047231814,"4.94392","-10.95901","-0.498763"
"user_001","Jumping",1446047231878,"3.75599","-19.27452","-1.76333"
"user_001","Jumping",1446047231944,"7.70298","-19.58108","-2.95126"
"user_001","Jumping",1446047232008,"6.55337","-17.51178","-1.91661"
"user_001","Jumping",1446047232073,"2.10822","-2.03038","-0.268841"
"user_001","Jumping",1446047232138,"-1.68549","3.67935","-0.881966"
"user_001","Jumping",1446047232203,"0.15388","-0.114362","0.076042"
"user_001","Jumping",1446047232267,"2.79798","-1.95374","0.689167"
"user_001","Jumping",1446047232332,"1.45677","-0.957409","0.420925"
"user_001","Jumping",1446047232397,"1.18853","-2.95007","0.650847"
"user_001","Jumping",1446047232462,"3.44943","-13.87135","-3.71767"
"user_001","Jumping",1446047232526,"3.79431","-19.58108","-0.345482"
"user_001","Jumping",1446047232591,"6.09353","-19.58108","2.87342"
"user_001","Jumping",1446047232655,"4.48408","-14.3312","-0.498763"
"user_001","Jumping",1446047232720,"-0.037722","-1.68549","2.6435"
"user_001","Jumping",1446047232785,"0.613724","2.68302","-2.37646"
"user_001","Jumping",1446047232850,"0.996927","-0.497565","0.497565"
"user_001","Jumping",1446047232915,"0.15388","-0.689167","1.34061"
"user_001","Jumping",1446047232979,"1.07357","1.07357","-2.60638"
"user_001","Jumping",1446047233044,"2.37646","-3.29495","-1.45677"
"user_001","Jumping",1446047233109,"2.22318","-16.09393","1.37893"
"user_001","Jumping",1446047233173,"6.82161","-19.58108","0.037722"
"user_001","Jumping",1446047233238,"8.39275","-19.54276","1.22565"
"user_001","Jumping",1446047233303,"1.22685","-9.50284","-0.307161"
"user_001","Jumping",1446047233368,"1.83997","-0.152682","1.41725"
"user_001","Jumping",1446047233432,"-0.765807","0.996927","-1.91661"
"user_001","Jumping",1446047233497,"0.728685","-0.612526","0.152682"
"user_001","Jumping",1446047233562,"0.996927","-0.305964","0.995729"
"user_001","Jumping",1446047233626,"1.22685","-0.305964","0.037722"
"user_001","Jumping",1446047233692,"1.91661","-2.95007","-0.345482"
"user_001","Jumping",1446047233755,"4.75232","-14.63776","-3.67935"
"user_001","Jumping",1446047233821,"4.56072","-19.58108","-2.6447"
"user_001","Jumping",1446047233886,"10.69197","-19.38948","-5.94025"
"user_001","Jumping",1446047233950,"3.10454","-10.42253","-2.22318"
"user_001","Jumping",1446047234015,"-0.152682","1.61005","1.80046"
"user_001","Jumping",1446047234080,"-0.267643","1.83997","-0.958607"
"user_001","Jumping",1446047234145,"1.99325","-0.842448","-0.230521"
"user_001","Jumping",1446047234210,"2.79798","-0.114362","0.919089"
"user_001","Jumping",1446047234273,"1.11189","-0.152682","1.83878"
"user_001","Jumping",1446047234339,"2.75966","-5.05768","-0.537083"
"user_001","Jumping",1446047234403,"4.33079","-16.82202","-3.06622"
"user_001","Jumping",1446047234468,"6.89825","-19.58108","-4.25415"
"user_001","Jumping",1446047234533,"11.49669","-19.50444","-2.52974"
"user_001","Jumping",1446047234598,"6.20849","-9.08132","-1.80165"
"user_001","Jumping",1446047234663,"0.805325","1.15021","-0.15388"
"user_001","Jumping",1446047234726,"-1.76214","2.14654","-0.460442"
"user_001","Jumping",1446047234792,"0.000599","-1.11069","-0.230521"
"user_001","Jumping",1446047234857,"0.652044","0.230521","-0.920286"
"user_001","Jumping",1446047234922,"1.26517","-0.114362","-0.000599"
"user_001","Jumping",1446047234986,"5.36544","-4.09967","-0.537083"
"user_001","Jumping",1446047235051,"6.51505","-17.05194","-3.56439"
"user_001","Jumping",1446047235115,"6.93658","-19.58108","-2.75966"
"user_001","Jumping",1446047235181,"7.89458","-19.58108","-3.18118"
"user_001","Jumping",1446047235245,"1.95493","-9.6178","-1.34181"
"user_001","Jumping",1446047235310,"-2.18366","3.14286","-0.1922"
"user_001","Jumping",1446047235375,"-2.87342","1.99325","-1.76333"
"user_001","Jumping",1446047235439,"1.03525","-1.80046","1.64717"
"user_001","Jumping",1446047235504,"1.95493","-0.689167","1.49389"
"user_001","Jumping",1446047235569,"1.26517","0.383802","-0.307161"
"user_001","Jumping",1446047235633,"1.18853","-2.72014","1.41725"
"user_001","Jumping",1446047235698,"5.71033","-16.51545","-2.03158"
"user_001","Jumping",1446047235763,"6.55337","-19.58108","0.995729"
"user_001","Jumping",1446047235828,"10.577","-19.4278","-1.57173"
"user_001","Jumping",1446047235892,"2.79798","-9.88604","-0.843646"
"user_001","Jumping",1446047235957,"0.652044","-0.037722","1.14901"
"user_001","Jumping",1446047236022,"0.843646","1.95493","-2.14654"
"user_001","Jumping",1446047236086,"0.15388","-0.305964","0.995729"
"user_001","Jumping",1446047236152,"0.767005","-0.114362","0.804128"
"user_001","Jumping",1446047236215,"-0.650847","-0.650847","-0.230521"
"user_001","Jumping",1446047236281,"3.56439","-5.90073","0.727487"
"user_001","Jumping",1446047236346,"7.05154","-17.97163","-1.61005"
"user_001","Jumping",1446047236410,"9.46572","-19.58108","2.10702"
"user_001","Jumping",1446047236475,"7.58802","-18.54643","3.10335"
"user_001","Jumping",1446047236540,"0.767005","-6.28393","0.689167"
"user_001","Jumping",1446047236604,"0.307161","2.29982","-0.498763"
"user_001","Jumping",1446047236669,"0.11556","-0.114362","-1.11189"
"user_001","Jumping",1446047236734,"2.2615","-1.45557","2.14534"
"user_001","Jumping",1446047236798,"1.80165","0.345482","0.765807"
"user_001","Jumping",1446047236863,"-0.650847","-0.305964","-1.15021"
"user_001","Jumping",1446047236928,"3.48775","-7.31858","-0.728685"
"user_001","Jumping",1446047236992,"5.32712","-18.92964","0.114362"
"user_001","Jumping",1446047237057,"10.34708","-19.58108","2.37526"
"user_001","Jumping",1446047237122,"5.97857","-17.78002","0.344284"
"user_001","Jumping",1446047237187,"2.8363","-3.25663","0.152682"
"user_001","Jumping",1446047237252,"-0.995729","3.37279","-1.64837"
"user_001","Jumping",1446047237317,"-1.64717","-0.650847","1.41725"
"user_001","Jumping",1446047237381,"1.30349","-0.152682","1.45557"
"user_001","Jumping",1446047237446,"2.79798","0.000599","-0.460442"
"user_001","Jumping",1446047237510,"1.41845","-0.459245","-1.38013"
"user_001","Jumping",1446047237575,"3.90927","-6.62882","-1.49509"
"user_001","Jumping",1446047237640,"4.98224","-19.27452","1.57053"
"user_001","Jumping",1446047237705,"12.22478","-19.58108","2.0687"
"user_001","Jumping",1446047237769,"4.5224","-16.74538","-0.383802"
"user_001","Jumping",1446047237834,"0.690364","-0.880768","-0.537083"
"user_001","Jumping",1446047237899,"1.26517","2.18486","-1.03525"
"user_001","Jumping",1446047237964,"2.6447","-0.037722","1.18733"
"user_001","Jumping",1446047238028,"2.56806","-0.114362","1.03405"
"user_001","Jumping",1446047238094,"-1.14901","0.11556","-0.307161"
"user_001","Jumping",1446047238157,"-0.574206","-0.114362","-0.920286"
"user_001","Jumping",1446047238223,"2.60638","-3.40991","-0.422122"
"user_001","Jumping",1446047238287,"6.32345","-17.62674","-0.728685"
"user_001","Jumping",1446047238352,"10.53868","-19.58108","0.919089"
"user_001","Jumping",1446047238417,"7.85626","-18.20155","0.995729"
"user_001","Jumping",1446047238481,"-0.152682","-1.41725","-1.22685"
"user_001","Jumping",1446047238546,"1.26517","2.22318","-1.87829"
"user_001","Jumping",1446047238611,"0.537083","-0.497565","1.76214"
"user_001","Jumping",1446047238676,"1.95493","0.230521","1.60885"
"user_001","Jumping",1446047238741,"0.920286","0.537083","-0.422122"
"user_001","Jumping",1446047238806,"0.000599","-0.650847","0.497565"
"user_001","Jumping",1446047238870,"1.64837","-1.99206","-1.72501"
"user_001","Jumping",1446047238935,"5.0972","-14.75272","-1.41845"
"user_001","Jumping",1446047239000,"10.50036","-19.58108","1.91542"
"user_001","Jumping",1446047239064,"8.16283","-19.58108","1.95374"
"user_001","Jumping",1446047239129,"3.56439","-9.4262","-1.22685"
"user_001","Jumping",1446047239194,"0.920286","2.79798","-1.34181"
"user_001","Jumping",1446047239259,"0.1922","0.652044","-0.537083"
"user_001","Jumping",1446047239324,"1.64837","-0.957409","1.41725"
"user_001","Jumping",1446047239388,"1.99325","-0.191003","0.957409"
"user_001","Jumping",1446047239453,"1.26517","-0.305964","-0.000599"
"user_001","Jumping",1446047239518,"2.4531","-4.17632","-1.34181"
"user_001","Jumping",1446047239583,"4.44576","-16.05561","-0.537083"
"user_001","Jumping",1446047239647,"9.58068","-19.58108","1.53221"
"user_001","Jumping",1446047239712,"8.04786","-19.2362","2.0687"
"user_001","Jumping",1446047239777,"1.80165","-6.01569","-0.422122"
"user_001","Jumping",1446047239841,"0.11556","2.10822","-2.6447"
"user_001","Jumping",1446047239906,"0.345482","-0.612526","-1.22685"
"user_001","Jumping",1446047239971,"1.45677","-0.880768","2.2603"
"user_001","Jumping",1446047240036,"0.843646","1.11189","-0.537083"
"user_001","Jumping",1446047240100,"0.996927","0.038919","-0.575403"
"user_001","Jumping",1446047240166,"3.29615","-3.98471","-0.422122"
"user_001","Jumping",1446047240231,"5.21216","-16.7837","-0.613724"
"user_001","Jumping",1446047240295,"7.39642","-19.58108","1.11069"
"user_001","Jumping",1446047240360,"10.23212","-19.58108","0.459245"
"user_001","Jumping",1446047240424,"1.80165","-12.14694","-1.22685"
"user_001","Jumping",1446047240489,"0.575403","1.83997","0.037722"
"user_001","Jumping",1446047240553,"-0.152682","2.22318","-3.0279"
"user_001","Jumping",1446047240618,"0.268841","-1.91542","3.06503"
"user_001","Jumping",1446047240683,"1.72501","-0.535886","1.83878"
"user_001","Jumping",1446047240748,"-1.18733","0.996927","-1.26517"
"user_001","Jumping",1446047240813,"0.920286","-0.535886","-1.91661"
"user_001","Jumping",1446047240878,"4.40743","-11.61046","0.459245"
"user_001","Jumping",1446047240941,"6.78329","-19.58108","4.82776"
"user_001","Jumping",1446047241007,"9.35075","-19.58108","5.74745"
"user_001","Jumping",1446047241072,"5.71033","-15.74905","-1.15021"
"user_001","Jumping",1446047241136,"-1.57053","-1.80046","-0.15388"
"user_001","Jumping",1446047241201,"-0.267643","2.18486","-2.14654"
"user_001","Jumping",1446047241266,"2.22318","-1.80046","1.80046"
"user_001","Jumping",1446047241330,"-1.34061","0.767005","-2.03158"
"user_001","Jumping",1446047241395,"-0.382604","-0.842448","0.382604"
"user_001","Jumping",1446047241459,"-0.765807","-1.41725","0.076042"
"user_001","Jumping",1446047241525,"5.97857","-10.34589","-1.49509"
"user_001","Jumping",1446047241589,"11.34341","-19.58108","6.13065"
"user_001","Jumping",1446047241654,"10.15548","-19.58108","8.58315"
"user_001","Jumping",1446047241718,"4.67568","-11.61046","-0.307161"
"user_001","Jumping",1446047241783,"-1.37893","-0.650847","0.459245"
"user_001","Jumping",1446047241849,"-0.919089","2.6447","-3.14286"
"user_001","Jumping",1446047241913,"1.07357","-1.03405","-0.613724"
"user_001","Jumping",1446047241977,"0.15388","-0.919089","2.18366"
"user_001","Jumping",1446047242042,"-0.995729","0.460442","0.305964"
"user_001","Jumping",1446047242107,"2.03158","-0.382604","0.076042"
"user_001","Jumping",1446047242172,"6.93658","-10.84405","-0.422122"
"user_001","Jumping",1446047242237,"13.79591","-19.58108","1.49389"
"user_001","Jumping",1446047242302,"15.86521","-19.50444","-1.72501"
"user_001","Jumping",1446047242366,"4.79064","-12.03198","1.8771"
"user_001","Jumping",1446047242431,"-0.497565","1.45677","-4.10087"
"user_001","Jumping",1446047242495,"0.11556","0.613724","-1.30349"
"user_001","Jumping",1446047242560,"-0.459245","-0.305964","2.56686"
"user_001","Jumping",1446047242625,"2.29982","1.18853","-0.767005"
"user_001","Jumping",1446047242690,"1.38013","-0.305964","-1.87829"
"user_001","Jumping",1446047242754,"2.52974","-2.75846","0.152682"
"user_001","Jumping",1446047242819,"5.32712","-13.21991","-1.34181"
"user_001","Jumping",1446047242884,"10.50036","-19.58108","1.8771"
"user_001","Jumping",1446047242948,"13.02951","-19.58108","-0.498763"
"user_001","Jumping",1446047243013,"4.33079","-10.34589","-2.4531"
"user_001","Jumping",1446047243078,"0.613724","0.498763","2.49022"
"user_001","Jumping",1446047243143,"0.307161","1.53341","-2.49142"
"user_001","Jumping",1446047243207,"0.077239","-0.344284","1.91542"
"user_001","Jumping",1446047243272,"2.03158","0.1922","-0.000599"
"user_001","Jumping",1446047243337,"1.22685","0.230521","-1.87829"
"user_001","Jumping",1446047243401,"0.537083","-1.26397","0.535886"
"user_001","Jumping",1446047243467,"5.2888","-11.22725","0.689167"
"user_001","Jumping",1446047243531,"11.57333","-19.58108","0.344284"
"user_001","Jumping",1446047243596,"14.524","-19.58108","2.33694"
"user_001","Jumping",1446047243661,"3.18118","-11.8787","0.191003"
"user_001","Jumping",1446047243725,"-1.07237","1.15021","0.152682"
"user_001","Jumping",1446047243790,"0.422122","1.95493","-1.87829"
"user_001","Jumping",1446047243855,"-0.535886","-0.957409","1.18733"
"user_001","Jumping",1446047243920,"3.60271","-0.114362","0.574206"
"user_001","Jumping",1446047243984,"2.87462","-0.535886","-0.268841"
"user_001","Jumping",1446047244049,"2.2615","-1.53221","-0.11556"
"user_001","Jumping",1446047244114,"6.85994","-10.61413","-1.11189"
"user_001","Jumping",1446047244178,"7.97122","-19.58108","-0.077239"
"user_001","Jumping",1446047244243,"14.86888","-19.58108","-1.41845"
"user_001","Jumping",1446047244308,"8.50771","-14.48448","-0.613724"
"user_001","Jumping",1446047244373,"-0.535886","-1.18733","1.64717"
"user_001","Jumping",1446047244437,"-4.5212","3.37279","0.191003"
"user_001","Jumping",1446047244502,"-1.72382","0.690364","0.191003"
"user_003","Jumping",1446047778034,"-2.75846","-7.28026","-1.45677"
"user_003","Jumping",1446047778099,"-3.75479","-7.08866","-2.37646"
"user_003","Jumping",1446047778164,"-2.14534","-6.74378","-2.22318"
"user_003","Jumping",1446047778229,"-2.22198","-6.01569","-2.2615"
"user_003","Jumping",1446047778293,"-3.63983","-9.04299","-2.6447"
"user_003","Jumping",1446047778358,"-4.82776","-17.09026","-4.02423"
"user_003","Jumping",1446047778422,"-5.82409","-19.46612","-6.70665"
"user_003","Jumping",1446047778488,"0.805325","-10.92069","-1.49509"
"user_003","Jumping",1446047778552,"-4.67448","1.22685","1.64717"
"user_003","Jumping",1446047778616,"-1.37893","0.460442","-2.18486"
"user_003","Jumping",1446047778682,"0.1922","-0.650847","-0.077239"
"user_003","Jumping",1446047778746,"-0.842448","0.11556","-1.26517"
"user_003","Jumping",1446047778811,"-1.26397","-11.91702","-3.64103"
"user_003","Jumping",1446047778876,"-1.41725","-19.58108","-0.767005"
"user_003","Jumping",1446047778940,"-1.76214","-15.44249","-2.68302"
"user_003","Jumping",1446047779006,"-0.574206","-9.00468","-1.76333"
"user_003","Jumping",1446047779071,"-3.14167","-4.36792","-1.68669"
"user_003","Jumping",1446047779135,"-3.37159","-4.3296","-2.4531"
"user_003","Jumping",1446047779200,"-4.06135","-4.75112","-3.10454"
"user_003","Jumping",1446047779265,"-4.36792","-11.26557","-0.345482"
"user_003","Jumping",1446047779329,"-5.97737","-19.12124","-4.25415"
"user_003","Jumping",1446047779394,"-3.75479","-19.19788","-4.9056"
"user_003","Jumping",1446047779459,"-0.459245","-7.39522","-0.537083"
"user_003","Jumping",1446047779524,"-2.91174","3.10454","-2.37646"
"user_003","Jumping",1446047779589,"-1.41725","-0.995729","-0.230521"
"user_003","Jumping",1446047779653,"0.422122","-0.114362","0.344284"
"user_003","Jumping",1446047779718,"-0.574206","-0.535886","-1.18853"
"user_003","Jumping",1446047779783,"-1.72382","-13.98632","-4.10087"
"user_003","Jumping",1446047779847,"-3.14167","-19.58108","-3.48775"
"user_003","Jumping",1446047779912,"-2.68182","-13.21991","-3.18118"
"user_003","Jumping",1446047779977,"-0.267643","-8.92803","-2.10822"
"user_003","Jumping",1446047780042,"-3.17999","-3.98471","-0.728685"
"user_003","Jumping",1446047780107,"-2.91174","-4.48288","-2.18486"
"user_003","Jumping",1446047780171,"-3.37159","-5.78577","-3.87095"
"user_003","Jumping",1446047780236,"-4.02303","-9.84772","-0.383802"
"user_003","Jumping",1446047780301,"-6.47553","-17.81835","-4.13919"
"user_003","Jumping",1446047780365,"-3.86975","-19.58108","-5.40376"
"user_003","Jumping",1446047780430,"-1.99206","-14.44616","-3.64103"
"user_003","Jumping",1446047780495,"-2.29862","-1.18733","-0.307161"
"user_003","Jumping",1446047780559,"-1.8771","1.99325","-1.68669"
"user_003","Jumping",1446047780625,"0.000599","-0.344284","0.650847"
"user_003","Jumping",1446047780689,"-0.459245","-0.191003","-0.613724"
"user_003","Jumping",1446047780754,"-2.60518","-6.9737","-4.714"
"user_003","Jumping",1446047780818,"-1.41725","-19.58108","-2.56806"
"user_003","Jumping",1446047780884,"-3.40991","-17.51178","-4.59904"
"user_003","Jumping",1446047780948,"-1.95374","-10.61413","-2.91294"
"user_003","Jumping",1446047781013,"-3.06503","-6.70546","-1.76333"
"user_003","Jumping",1446047781078,"-2.6435","-7.20362","-1.87829"
"user_003","Jumping",1446047781143,"-2.60518","-7.28026","-1.64837"
"user_003","Jumping",1446047781208,"-1.72382","-6.51385","-2.0699"
"user_003","Jumping",1446047781272,"-0.689167","-5.55585","-2.37646"
"user_003","Jumping",1446047781337,"-3.67815","-8.00835","-1.99325"
"user_003","Jumping",1446047781402,"-5.63249","-13.64143","-3.44943"
"user_003","Jumping",1446047781467,"-5.97737","-18.16323","-5.59536"
"user_003","Jumping",1446047781531,"-2.03038","-19.2362","-5.25048"
"user_003","Jumping",1446047781596,"-2.14534","-8.88971","-1.18853"
"user_003","Jumping",1446047781660,"-3.06503","2.52974","-0.767005"
"user_003","Jumping",1446047781725,"0.230521","0.268841","-0.345482"
"user_003","Jumping",1446047781790,"0.15388","-0.535886","-0.690364"
"user_003","Jumping",1446047781855,"-0.420925","-2.18366","-2.68302"
"user_003","Jumping",1446047781919,"-3.63983","-17.47346","-4.44576"
"user_003","Jumping",1446047781985,"-2.91174","-18.35483","-4.714"
"user_003","Jumping",1446047782049,"-0.919089","-10.88237","-2.2615"
"user_003","Jumping",1446047782114,"-3.02671","-6.93538","-2.68302"
"user_003","Jumping",1446047782178,"-2.75846","-7.3569","-2.10822"
"user_003","Jumping",1446047782244,"-1.45557","-7.85507","-1.22685"
"user_003","Jumping",1446047782309,"-1.8771","-6.32225","-1.99325"
"user_003","Jumping",1446047782373,"-1.64717","-5.90073","-2.33814"
"user_003","Jumping",1446047782438,"-2.49022","-8.08499","-1.80165"
"user_003","Jumping",1446047782503,"-4.40624","-11.26557","-2.37646"
"user_003","Jumping",1446047782568,"-5.13432","-17.1669","-4.67568"
"user_003","Jumping",1446047782632,"-3.40991","-19.54276","-5.71033"
"user_003","Jumping",1446047782697,"-1.99206","-13.48815","-4.06255"
"user_003","Jumping",1446047782762,"-2.22198","-1.68549","0.267643"
"user_003","Jumping",1446047782827,"-1.91542","1.80165","-1.68669"
"user_003","Jumping",1446047782891,"0.575403","-0.152682","0.459245"
"user_003","Jumping",1446047782956,"-0.842448","-0.804128","-1.03525"
"user_003","Jumping",1446047783021,"-1.76214","-12.22358","-5.67201"
"user_003","Jumping",1446047783086,"-2.33694","-19.19788","-4.02423"
"user_003","Jumping",1446047783151,"-1.14901","-14.98264","-4.13919"
"user_003","Jumping",1446047783215,"-2.22198","-9.04299","-2.91294"
"user_003","Jumping",1446047783280,"-2.41358","-5.59417","-0.613724"
"user_003","Jumping",1446047783344,"-2.60518","-5.78577","-2.33814"
"user_003","Jumping",1446047783409,"-3.67815","-4.44456","-2.4531"
"user_003","Jumping",1446047783474,"-2.8351","-4.63616","-3.75599"
"user_003","Jumping",1446047783539,"-1.64717","-16.4005","-0.843646"
"user_003","Jumping",1446047783604,"-6.62882","-19.58108","-8.58435"
"user_003","Jumping",1446047783669,"-1.72382","-14.67608","-3.52607"
"user_003","Jumping",1446047783733,"-1.57053","1.34181","0.612526"
"user_003","Jumping",1446047783798,"-1.18733","1.22685","-1.38013"
"user_003","Jumping",1446047783863,"-0.612526","-0.612526","-0.268841"
"user_003","Jumping",1446047783928,"-1.22565","0.345482","-0.805325"
"user_003","Jumping",1446047783992,"-1.11069","-2.2603","-2.56806"
"user_003","Jumping",1446047784057,"-2.8351","-17.62674","-5.02056"
"user_003","Jumping",1446047784122,"-3.90807","-19.19788","-5.59536"
"user_003","Jumping",1446047784187,"-1.72382","-12.6451","-3.64103"
"user_003","Jumping",1446047784252,"-1.49389","-6.7821","-2.0699"
"user_003","Jumping",1446047784316,"-2.14534","-4.67448","-1.15021"
"user_003","Jumping",1446047784381,"-2.91174","-6.93538","-2.6447"
"user_003","Jumping",1446047784446,"-3.71647","-5.86241","-2.56806"
"user_003","Jumping",1446047784510,"-0.344284","-6.51385","-2.8363"
"user_003","Jumping",1446047784575,"-4.02303","-14.98264","-2.2615"
"user_003","Jumping",1446047784639,"-5.36425","-19.58108","-8.89091"
"user_003","Jumping",1446047784704,"-0.191003","-14.906","-3.94759"
"user_003","Jumping",1446047784769,"-2.4519","0.077239","1.11069"
"user_003","Jumping",1446047784834,"-2.4519","1.22685","-1.41845"
"user_003","Jumping",1446047784899,"-0.344284","0.345482","-0.15388"
"user_003","Jumping",1446047784964,"-0.842448","0.422122","-0.996927"
"user_003","Jumping",1446047785029,"-2.29862","-4.3296","-4.63736"
"user_003","Jumping",1446047785093,"-0.765807","-18.92964","-3.98591"
"user_003","Jumping",1446047785158,"-4.3296","-19.38948","-5.17384"
"user_003","Jumping",1446047785222,"-1.11069","-12.6451","-1.22685"
"user_003","Jumping",1446047785287,"-3.44823","-7.24194","-2.18486"
"user_003","Jumping",1446047785352,"-2.49022","-6.51385","-2.95126"
"user_003","Jumping",1446047785417,"-2.10702","-6.85874","-1.22685"
"user_003","Jumping",1446047785482,"-2.79678","-7.05034","-2.14654"
"user_003","Jumping",1446047785546,"-2.56686","-5.01936","-2.52974"
"user_003","Jumping",1446047785611,"-2.87342","-6.62882","-2.56806"
"user_003","Jumping",1446047785676,"-4.63616","-14.44616","-2.52974"
"user_003","Jumping",1446047785741,"-5.67081","-19.58108","-6.59169"
"user_003","Jumping",1446047785805,"-3.67815","-17.66507","-6.63001"
"user_003","Jumping",1446047785870,"-0.919089","-4.82776","-0.268841"
"user_003","Jumping",1446047785934,"-2.91174","2.87462","-2.33814"
"user_003","Jumping",1446047785999,"-0.420925","-0.420925","0.919089"
"user_003","Jumping",1446047786065,"-0.037722","0.230521","0.382604"
"user_003","Jumping",1446047786129,"-0.842448","-0.497565","-3.0279"
"user_003","Jumping",1446047786194,"-0.995729","-15.48081","-4.33079"
"user_003","Jumping",1446047786259,"-3.98471","-19.50444","-6.89825"
"user_003","Jumping",1446047786324,"-0.574206","-13.29655","-3.94759"
"user_003","Jumping",1446047786388,"-1.41725","-8.85139","-1.38013"
"user_003","Jumping",1446047786453,"-3.94639","-5.59417","-3.14286"
"user_003","Jumping",1446047786518,"-2.18366","-8.04667","-3.06622"
"user_003","Jumping",1446047786582,"-1.60885","-8.92803","-1.15021"
"user_003","Jumping",1446047786647,"-2.72014","-5.13432","-2.33814"
"user_003","Jumping",1446047786712,"-2.03038","-4.25296","-3.18118"
"user_003","Jumping",1446047786777,"-3.48655","-9.84772","-1.45677"
"user_003","Jumping",1446047786841,"-6.01569","-18.96796","-6.78329"
"user_003","Jumping",1446047786906,"-4.44456","-19.4278","-8.62267"
"user_003","Jumping",1446047786970,"-0.267643","-8.92803","-1.57173"
"user_003","Jumping",1446047787035,"-2.91174","3.41111","-1.53341"
"user_003","Jumping",1446047787100,"0.268841","-0.765807","0.689167"
"user_003","Jumping",1446047787165,"-0.650847","0.383802","-0.728685"
"user_003","Jumping",1446047787230,"-0.880768","0.422122","-1.03525"
"user_003","Jumping",1446047787294,"-1.60885","-11.11229","-5.4804"
"user_003","Jumping",1446047787358,"-4.59784","-19.35116","-5.94025"
"user_003","Jumping",1446047787424,"-2.41358","-15.71073","-5.74865"
"user_003","Jumping",1446047787489,"-1.26397","-9.34956","-3.06622"
"user_003","Jumping",1446047787553,"-3.44823","-6.47553","-2.75966"
"user_003","Jumping",1446047787618,"-2.4519","-7.97003","-2.2615"
"user_003","Jumping",1446047787683,"-1.95374","-7.81675","-1.83997"
"user_003","Jumping",1446047787747,"-2.75846","-4.67448","-3.06622"
"user_003","Jumping",1446047787812,"-2.22198","-5.24928","-2.0699"
"user_003","Jumping",1446047787877,"-3.29495","-12.41518","-1.49509"
"user_003","Jumping",1446047787942,"-5.44089","-19.46612","-6.20849"
"user_003","Jumping",1446047788007,"-3.79311","-18.69972","-7.70298"
"user_003","Jumping",1446047788071,"-0.382604","-7.1653","-1.83997"
"user_003","Jumping",1446047788136,"-3.14167","2.72134","-0.575403"
"user_003","Jumping",1446047788201,"0.460442","-0.152682","0.114362"
"user_003","Jumping",1446047788265,"-0.842448","0.652044","-0.230521"
"user_003","Jumping",1446047788331,"-2.2603","-0.229323","-1.45677"
"user_003","Jumping",1446047788395,"-1.45557","-13.71807","-6.63001"
"user_003","Jumping",1446047788460,"-3.14167","-19.54276","-6.32345"
"user_003","Jumping",1446047788524,"-1.83878","-13.06663","-4.40743"
"user_003","Jumping",1446047788590,"-1.83878","-9.77108","-3.25783"
"user_003","Jumping",1446047788654,"-3.14167","-6.55217","-2.6447"
"user_003","Jumping",1446047788719,"-2.10702","-7.31858","-2.10822"
"user_003","Jumping",1446047788784,"-2.14534","-5.90073","-2.0699"
"user_003","Jumping",1446047788848,"-1.57053","-4.9044","-2.75966"
"user_003","Jumping",1446047788913,"-2.75846","-7.5485","-1.76333"
"user_003","Jumping",1446047788978,"-4.98104","-15.97897","-4.67568"
"user_003","Jumping",1446047789043,"-3.10335","-19.58108","-8.16283"
"user_003","Jumping",1446047789107,"-1.68549","-16.36218","-5.25048"
"user_003","Jumping",1446047789172,"-2.52854","-2.0687","-0.345482"
"user_003","Jumping",1446047789236,"-1.72382","2.41478","-2.37646"
"user_003","Jumping",1446047789301,"0.843646","-0.382604","1.11069"
"user_003","Jumping",1446047789366,"-0.689167","0.230521","-0.728685"
"user_003","Jumping",1446047789431,"-1.14901","-1.95374","-2.72134"
"user_003","Jumping",1446047789495,"-3.90807","-17.74171","-7.58802"
"user_003","Jumping",1446047789560,"-3.29495","-18.96796","-5.05888"
"user_003","Jumping",1446047789625,"-2.22198","-12.0703","-3.98591"
"user_003","Jumping",1446047789690,"-2.79678","-7.7401","-2.37646"
"user_003","Jumping",1446047789755,"-1.95374","-5.74745","-2.22318"
"user_003","Jumping",1446047789818,"-2.03038","-6.85874","-1.68669"
"user_003","Jumping",1446047789883,"-2.91174","-5.67081","-2.52974"
"user_003","Jumping",1446047789949,"-1.49389","-4.94272","-2.75966"
"user_003","Jumping",1446047790013,"-3.37159","-10.88237","-1.57173"
"user_003","Jumping",1446047790078,"-5.51753","-19.58108","-7.24314"
"user_003","Jumping",1446047790143,"-4.48288","-17.5501","-6.9749"
"user_003","Jumping",1446047790207,"-0.612526","-2.68182","-0.613724"
"user_003","Jumping",1446047790272,"-1.8771","2.68302","-2.03158"
"user_003","Jumping",1446047790337,"1.15021","-0.919089","1.64717"
"user_003","Jumping",1446047790402,"-2.03038","0.307161","-1.22685"
"user_003","Jumping",1446047790467,"-1.34061","-0.191003","-1.61005"
"user_003","Jumping",1446047790531,"-3.02671","-13.64143","-5.17384"
"user_003","Jumping",1446047790596,"-4.75112","-19.58108","-7.62634"
"user_003","Jumping",1446047790660,"-2.60518","-15.05928","-6.01689"
"user_003","Jumping",1446047790726,"-1.26397","-8.12331","-3.10454"
"user_003","Jumping",1446047790790,"-3.25663","-5.97737","-3.0279"
"user_003","Jumping",1446047790855,"-0.420925","-6.24561","-2.2615"
"user_003","Jumping",1446047790919,"-1.83878","-3.40991","-3.60271"
"user_003","Jumping",1446047790984,"-3.94639","-5.36425","-3.75599"
"user_003","Jumping",1446047791049,"-2.2603","-13.98632","-3.67935"
"user_003","Jumping",1446047791114,"-7.70178","-19.58108","-7.5497"
"user_003","Jumping",1446047791179,"-2.52854","-15.36585","-6.66833"
"user_003","Jumping",1446047791243,"-0.574206","-0.497565","-0.230521"
"user_003","Jumping",1446047791308,"-2.0687","0.958607","-0.575403"
"user_003","Jumping",1446047791373,"0.11556","-0.497565","0.344284"
"user_003","Jumping",1446047791438,"-1.30229","1.34181","-1.11189"
"user_003","Jumping",1446047791503,"-0.382604","-0.880768","-2.22318"
"user_003","Jumping",1446047791568,"-2.87342","-15.97897","-6.28513"
"user_003","Jumping",1446047791632,"-2.95007","-19.38948","-7.51138"
"user_003","Jumping",1446047791697,"-4.59784","-13.37319","-5.02056"
"user_003","Jumping",1446047791762,"-2.14534","-9.84772","-3.2195"
"user_003","Jumping",1446047791827,"-2.29862","-6.55217","-2.49142"
"user_003","Jumping",1446047791891,"-1.68549","-7.47186","-2.29982"
"user_003","Jumping",1446047791956,"-1.22565","-8.16163","-2.56806"
"user_003","Jumping",1446047792021,"-2.95007","-5.096","-2.52974"
"user_003","Jumping",1446047792085,"-4.02303","-4.06135","-7.7413"
"user_003","Jumping",1446047792151,"-2.60518","-14.40784","0.727487"
"user_003","Jumping",1446047792215,"-6.24561","-19.58108","-8.89091"
"user_003","Jumping",1446047792280,"-1.60885","-12.56846","-3.2195"
"user_003","Jumping",1446047792344,"-3.10335","3.14286","-1.26517"
"user_003","Jumping",1446047792410,"-0.037722","0.000599","-0.11556"
"user_003","Jumping",1446047792474,"0.268841","-0.650847","0.114362"
"user_003","Jumping",1446047792539,"-2.68182","0.996927","-1.99325"
"user_003","Jumping",1446047792603,"-1.64717","-4.67448","-5.51872"
"user_003","Jumping",1446047792669,"-2.87342","-18.96796","-5.86361"
"user_003","Jumping",1446047792733,"-3.29495","-18.66139","-4.44576"
"user_003","Jumping",1446047792798,"-4.02303","-13.21991","-4.79064"
"user_003","Jumping",1446047792863,"-2.95007","-7.97003","-2.79798"
"user_003","Jumping",1446047792928,"-3.17999","-6.20729","-2.29982"
"user_003","Jumping",1446047792992,"-3.25663","-5.40257","-2.37646"
"user_003","Jumping",1446047793057,"-3.40991","-4.5212","-3.18118"
"user_003","Jumping",1446047793122,"-3.29495","-3.90807","-3.94759"
"user_003","Jumping",1446047793186,"-4.7128","-14.7144","-2.75966"
"user_003","Jumping",1446047793251,"-4.55952","-19.58108","-7.77962"
"user_003","Jumping",1446047793316,"-2.95007","-16.32385","-6.93658"
"user_003","Jumping",1446047793380,"-1.26397","-2.14534","-1.38013"
"user_003","Jumping",1446047793445,"-2.10702","2.33814","-1.64837"
"user_003","Jumping",1446047793510,"-0.420925","-0.267643","1.07237"
"user_003","Jumping",1446047793575,"-0.995729","0.383802","-0.537083"
"user_003","Jumping",1446047793640,"-1.14901","-1.76214","-2.29982"
"user_003","Jumping",1446047793704,"-2.56686","-14.82936","-6.28513"
"user_003","Jumping",1446047793768,"-3.40991","-19.31284","-7.3581"
"user_003","Jumping",1446047793834,"-3.52487","-13.87135","-5.63368"
"user_003","Jumping",1446047793899,"-2.68182","-10.84405","-3.10454"
"user_003","Jumping",1446047793963,"-2.72014","-4.78944","-2.72134"
"user_003","Jumping",1446047794028,"-1.03405","-4.94272","-1.68669"
"user_003","Jumping",1446047794093,"-2.10702","-5.93905","-2.14654"
"user_003","Jumping",1446047794158,"-1.57053","-5.90073","-3.2195"
"user_003","Jumping",1446047794222,"-4.3296","-8.08499","-1.15021"
"user_003","Jumping",1446047794287,"-4.48288","-19.08292","-6.36177"
"user_003","Jumping",1446047794352,"-4.67448","-19.58108","-8.27779"
"user_003","Jumping",1446047794416,"0.000599","-10.76741","-2.33814"
"user_003","Jumping",1446047794481,"-1.95374","3.41111","-1.07357"
"user_003","Jumping",1446047794546,"-0.152682","-0.689167","0.229323"
"user_003","Jumping",1446047794611,"-0.957409","0.422122","-0.460442"
"user_003","Jumping",1446047794676,"-0.957409","0.996927","-0.881966"
"user_003","Jumping",1446047794740,"-1.57053","-3.60151","-4.75232"
"user_003","Jumping",1446047794805,"-1.99206","-18.35483","-5.78697"
"user_003","Jumping",1446047794870,"-3.71647","-19.2362","-6.47673"
"user_003","Jumping",1446047794935,"-0.842448","-14.94432","-4.17751"
"user_002","Standing",1446309439342,"-2.49022","-9.19628","-1.11189"
"user_002","Standing",1446309439349,"-2.41358","-9.15796","-1.22685"
"user_002","Standing",1446309439358,"-2.56686","-9.15796","-1.30349"
"user_002","Standing",1446309439365,"-2.75846","-9.08132","-1.11189"
"user_002","Standing",1446309439373,"-2.75846","-9.08132","-1.15021"
"user_002","Standing",1446309439382,"-2.8351","-9.15796","-0.920286"
"user_002","Standing",1446309439390,"-2.8357","-9.15855","-0.919687"
"user_002","Standing",1446309439398,"-2.95007","-9.2346","-0.881966"
"user_002","Standing",1446309439406,"-2.8351","-9.11964","-0.958607"
"user_002","Standing",1446309439414,"-2.8351","-8.96635","-1.15021"
"user_002","Standing",1446309439422,"-2.95007","-9.08132","-0.920286"
"user_002","Standing",1446309439430,"-2.95007","-8.88971","-0.996927"
"user_002","Standing",1446309439438,"-2.91174","-9.00468","-1.15021"
"user_002","Standing",1446309439447,"-3.02671","-8.96635","-0.996927"
"user_002","Standing",1446309439454,"-2.79678","-8.96635","-1.15021"
"user_002","Standing",1446309439463,"-2.87342","-9.08132","-1.34181"
"user_002","Standing",1446309439471,"-2.98839","-9.19628","-1.22685"
"user_002","Standing",1446309439479,"-2.95007","-9.00468","-1.30349"
"user_002","Standing",1446309439487,"-2.98839","-9.00468","-1.15021"
"user_002","Standing",1446309439495,"-3.06503","-9.00468","-0.958607"
"user_002","Standing",1446309439503,"-3.02671","-9.04299","-0.881966"
"user_002","Standing",1446309439511,"-2.91174","-9.00468","-0.920286"
"user_002","Standing",1446309439519,"-2.95007","-9.08132","-0.767005"
"user_002","Standing",1446309439527,"-2.8351","-9.00468","-0.958607"
"user_002","Standing",1446309439536,"-2.95007","-9.00468","-0.996927"
"user_002","Standing",1446309439543,"-2.75846","-9.04299","-0.690364"
"user_002","Standing",1446309439552,"-2.6435","-9.00468","-1.03525"
"user_002","Standing",1446309439560,"-2.72014","-9.00468","-1.15021"
"user_002","Standing",1446309439568,"-2.68182","-9.04299","-1.11189"
"user_002","Standing",1446309439576,"-2.60518","-9.00468","-1.11189"
"user_002","Standing",1446309439584,"-2.52854","-8.88971","-1.15021"
"user_002","Standing",1446309439593,"-2.6435","-8.92803","-1.03525"
"user_002","Standing",1446309439601,"-2.60518","-8.96635","-0.996927"
"user_002","Standing",1446309439609,"-2.6435","-9.04299","-0.996927"
"user_002","Standing",1446309439616,"-2.75846","-9.04299","-0.920286"
"user_002","Standing",1446309439624,"-2.6435","-9.19628","-0.805325"
"user_002","Standing",1446309439633,"-2.79678","-8.96635","-0.767005"
"user_002","Standing",1446309439641,"-2.87342","-8.92803","-0.958607"
"user_002","Standing",1446309439649,"-2.60518","-9.11964","-0.843646"
"user_002","Standing",1446309439657,"-2.8351","-9.08132","-0.843646"
"user_002","Standing",1446309439665,"-2.8351","-9.2346","-0.920286"
"user_002","Standing",1446309439674,"-2.72014","-9.11964","-0.958607"
"user_002","Standing",1446309439682,"-2.87342","-9.15796","-1.18853"
"user_002","Standing",1446309439690,"-3.06503","-9.2346","-1.18853"
"user_002","Standing",1446309439698,"-2.8351","-9.08132","-0.996927"
"user_002","Standing",1446309439705,"-2.98839","-9.04299","-1.03525"
"user_002","Standing",1446309439713,"-3.17999","-8.96635","-1.18853"
"user_002","Standing",1446309439722,"-3.21831","-8.88971","-1.18853"
"user_002","Standing",1446309439729,"-3.17999","-8.92803","-1.15021"
"user_002","Standing",1446309439738,"-3.17999","-8.77475","-1.22685"
"user_002","Standing",1446309439746,"-3.33327","-8.65979","-1.07357"
"user_002","Standing",1446309439754,"-3.37159","-8.58315","-1.34181"
"user_002","Standing",1446309439762,"-3.33327","-8.77475","-1.18853"
"user_002","Standing",1446309439771,"-3.33327","-8.58315","-1.07357"
"user_002","Standing",1446309439779,"-3.14167","-8.92803","-1.03525"
"user_002","Standing",1446309439787,"-3.25663","-8.85139","-0.881966"
"user_002","Standing",1446309439795,"-3.10335","-8.85139","-0.920286"
"user_002","Standing",1446309439803,"-2.87342","-8.85139","-0.843646"
"user_002","Standing",1446309439811,"-2.91174","-8.92803","-0.767005"
"user_002","Standing",1446309439819,"-2.87342","-8.88971","-0.805325"
"user_002","Standing",1446309439827,"-2.98839","-8.96635","-0.767005"
"user_002","Standing",1446309439835,"-2.79678","-9.11964","-1.07357"
"user_002","Standing",1446309439843,"-2.87342","-9.08132","-0.920286"
"user_002","Standing",1446309439851,"-2.95007","-9.15796","-0.805325"
"user_002","Standing",1446309439859,"-3.02671","-8.85139","-0.728685"
"user_002","Standing",1446309439868,"-2.98839","-8.92803","-0.690364"
"user_002","Standing",1446309439875,"-2.98839","-8.96635","-0.690364"
"user_002","Standing",1446309439883,"-2.91174","-8.96635","-0.881966"
"user_002","Standing",1446309439892,"-3.02671","-8.85139","-0.613724"
"user_002","Standing",1446309439900,"-3.06503","-9.04299","-0.767005"
"user_002","Standing",1446309439908,"-2.87342","-8.73643","-0.652044"
"user_002","Standing",1446309439916,"-2.95007","-8.88971","-0.728685"
"user_002","Standing",1446309439924,"-3.10335","-8.77475","-0.690364"
"user_002","Standing",1446309439932,"-2.91174","-8.77475","-0.652044"
"user_002","Standing",1446309439940,"-2.91174","-9.04299","-0.805325"
"user_002","Standing",1446309439948,"-2.91174","-9.04299","-0.767005"
"user_002","Standing",1446309439957,"-3.02671","-9.04299","-0.652044"
"user_002","Standing",1446309439965,"-3.06503","-9.27292","-0.805325"
"user_002","Standing",1446309439973,"-3.14167","-9.00468","-0.767005"
"user_002","Standing",1446309439980,"-2.95007","-8.96635","-0.843646"
"user_002","Standing",1446309439989,"-3.10335","-9.00468","-1.15021"
"user_002","Standing",1446309439997,"-2.98839","-9.19628","-1.03525"
"user_002","Standing",1446309440004,"-3.10335","-9.19628","-1.15021"
"user_002","Standing",1446309440013,"-2.91174","-9.11964","-1.18853"
"user_002","Standing",1446309440021,"-2.91174","-9.08132","-1.22685"
"user_002","Standing",1446309440029,"-2.95007","-9.00468","-1.26517"
"user_002","Standing",1446309440038,"-3.06503","-9.08132","-1.15021"
"user_002","Standing",1446309440045,"-3.06503","-9.00468","-1.03525"
"user_002","Standing",1446309440054,"-3.10335","-9.11964","-1.07357"
"user_002","Standing",1446309440062,"-3.10335","-8.96635","-0.996927"
"user_002","Standing",1446309440069,"-3.10335","-8.96635","-1.03525"
"user_002","Standing",1446309440078,"-3.17999","-9.00468","-1.11189"
"user_002","Standing",1446309440086,"-3.14167","-8.85139","-0.996927"
"user_002","Standing",1446309440094,"-3.17999","-9.00468","-0.728685"
"user_002","Standing",1446309440102,"-3.17999","-8.96635","-0.958607"
"user_002","Standing",1446309440111,"-3.10335","-9.00468","-0.881966"
"user_002","Standing",1446309440119,"-3.17999","-9.00468","-1.03525"
"user_002","Standing",1446309440126,"-3.02671","-9.00468","-0.881966"
"user_002","Standing",1446309440135,"-3.06503","-9.00468","-0.728685"
"user_002","Standing",1446309440142,"-3.21831","-8.92803","-1.03525"
"user_002","Standing",1446309440151,"-3.33327","-8.85139","-0.767005"
"user_002","Standing",1446309440158,"-3.21831","-9.04299","-0.652044"
"user_002","Standing",1446309440167,"-3.33327","-8.85139","-0.613724"
"user_002","Standing",1446309440175,"-3.37159","-8.85139","-0.345482"
"user_002","Standing",1446309440183,"-3.37159","-9.00468","-0.652044"
"user_002","Standing",1446309440192,"-3.33327","-8.81307","-0.460442"
"user_002","Standing",1446309440199,"-3.21831","-9.08132","-0.537083"
"user_002","Standing",1446309440208,"-3.21831","-8.92803","-0.690364"
"user_002","Standing",1446309440215,"-3.14167","-8.96635","-0.575403"
"user_002","Standing",1446309440224,"-3.17999","-8.92803","-0.690364"
"user_002","Standing",1446309440232,"-3.17999","-8.77475","-0.690364"
"user_002","Standing",1446309440239,"-3.14167","-8.88971","-0.805325"
"user_002","Standing",1446309440248,"-3.10335","-9.04299","-0.843646"
"user_002","Standing",1446309440255,"-3.14167","-8.88971","-0.767005"
"user_002","Standing",1446309440264,"-3.06503","-9.11964","-0.805325"
"user_002","Standing",1446309440272,"-3.14167","-8.96635","-0.690364"
"user_002","Standing",1446309440280,"-3.29495","-9.08132","-0.843646"
"user_002","Standing",1446309440288,"-3.33327","-8.81307","-0.575403"
"user_002","Standing",1446309440296,"-3.21831","-8.88971","-0.575403"
"user_002","Standing",1446309440305,"-3.21831","-8.96635","-0.613724"
"user_002","Standing",1446309440312,"-3.14167","-8.77475","-0.537083"
"user_002","Standing",1446309440320,"-3.10335","-8.85139","-0.613724"
"user_002","Standing",1446309440329,"-3.21831","-9.00468","-0.613724"
"user_002","Standing",1446309440337,"-3.14167","-8.85139","-0.613724"
"user_002","Standing",1446309440345,"-3.21831","-9.04299","-0.690364"
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"user_002","Standing",1446309440361,"-3.25663","-9.11964","-0.728685"
"user_002","Standing",1446309440369,"-3.21831","-8.92803","-0.652044"
"user_002","Standing",1446309440377,"-3.06503","-8.92803","-0.843646"
"user_002","Standing",1446309440385,"-3.06503","-9.04299","-0.690364"
"user_002","Standing",1446309440394,"-3.21831","-9.00468","-0.460442"
"user_002","Standing",1446309440402,"-3.02671","-8.92803","-0.652044"
"user_002","Standing",1446309440410,"-3.21831","-8.96635","-0.652044"
"user_002","Standing",1446309440417,"-3.25663","-8.96635","-0.767005"
"user_002","Standing",1446309440426,"-3.21831","-8.88971","-0.728685"
"user_002","Standing",1446309440434,"-3.37159","-8.85139","-0.767005"
"user_002","Standing",1446309440443,"-3.21831","-8.85139","-0.537083"
"user_002","Standing",1446309440450,"-3.10335","-9.11964","-0.767005"
"user_002","Standing",1446309440458,"-3.10335","-9.19628","-0.920286"
"user_002","Standing",1446309440467,"-3.10335","-9.00468","-0.767005"
"user_002","Standing",1446309440474,"-3.02671","-8.96635","-0.767005"
"user_002","Standing",1446309440482,"-3.10335","-8.92803","-0.805325"
"user_002","Standing",1446309440491,"-3.17999","-8.92803","-0.843646"
"user_002","Standing",1446309440498,"-3.33327","-8.96635","-0.805325"
"user_002","Standing",1446309440506,"-3.29495","-8.85139","-0.575403"
"user_002","Standing",1446309440515,"-3.21831","-8.88971","-0.728685"
"user_002","Standing",1446309440523,"-3.29495","-8.92803","-0.652044"
"user_002","Standing",1446309440531,"-3.37159","-9.04299","-0.575403"
"user_002","Standing",1446309440539,"-3.33327","-8.92803","-0.613724"
"user_002","Standing",1446309440547,"-3.33327","-8.88971","-0.498763"
"user_002","Standing",1446309440555,"-3.21831","-9.08132","-0.690364"
"user_002","Standing",1446309440564,"-3.06503","-8.88971","-0.652044"
"user_002","Standing",1446309440571,"-3.17999","-8.88971","-0.537083"
"user_002","Standing",1446309440580,"-3.06503","-8.88971","-0.498763"
"user_002","Standing",1446309440587,"-3.10335","-8.85139","-0.575403"
"user_002","Standing",1446309440596,"-3.06503","-9.08132","-0.613724"
"user_002","Standing",1446309440604,"-2.87342","-8.85139","-0.652044"
"user_002","Standing",1446309440612,"-2.95007","-9.08132","-0.728685"
"user_002","Standing",1446309440620,"-3.14167","-8.96635","-0.422122"
"user_002","Standing",1446309440628,"-3.02671","-8.92803","-0.537083"
"user_002","Standing",1446309440636,"-3.14167","-9.00468","-0.422122"
"user_002","Standing",1446309440645,"-3.10335","-9.08132","-0.383802"
"user_002","Standing",1446309440653,"-3.14167","-8.81307","-0.307161"
"user_002","Standing",1446309440660,"-3.17999","-8.85139","-0.422122"
"user_002","Standing",1446309440669,"-3.21831","-8.85139","-0.1922"
"user_002","Standing",1446309440676,"-3.17999","-9.04299","-0.498763"
"user_002","Standing",1446309440685,"-3.02671","-9.04299","-0.460442"
"user_002","Standing",1446309440693,"-3.21831","-8.85139","-0.498763"
"user_002","Standing",1446309440701,"-3.10335","-9.08132","-0.498763"
"user_002","Standing",1446309440709,"-3.29495","-8.96635","-0.537083"
"user_002","Standing",1446309440717,"-3.06503","-8.88971","-0.383802"
"user_002","Standing",1446309440725,"-3.10335","-9.15796","-0.498763"
"user_002","Standing",1446309440734,"-2.91174","-9.00468","-0.345482"
"user_002","Standing",1446309440741,"-3.17999","-8.88971","-0.498763"
"user_002","Standing",1446309440749,"-3.21831","-9.08132","-0.537083"
"user_002","Standing",1446309440757,"-3.06503","-9.00468","-0.460442"
"user_002","Standing",1446309440766,"-3.06503","-9.00468","-0.613724"
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"user_002","Standing",1446309440976,"-3.17999","-8.81307","-0.728685"
"user_002","Standing",1446309440985,"-2.98839","-8.88971","-0.575403"
"user_002","Standing",1446309440992,"-3.06503","-8.85139","-0.613724"
"user_002","S...

ScaDaMaLe Course site and book

Community Packages in Spark - more generally

Let us recall the following quoate in Chapter 10 of High Performance Spark book (needs access to Orielly publishers via your library/subscription): - https://learning.oreilly.com/library/view/high-performance-spark/9781491943199/ch10.html#components

Beyond the integrated components, the community packages can add important functionality to Spark, sometimes even superseding built-in functionality—like with GraphFrames.

Here we introduce you to GraphFrames quickly so you don't need to drop down to the GraphX library that requires more understanding of caching and checkpointing to keep the vertex program's DAG from exploding or becoming inefficient.

GraphFrames User Guide (Scala)

GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs. It provides high-level APIs in Scala, Java, and Python. It aims to provide both the functionality of GraphX and extended functionality taking advantage of Spark DataFrames. This extended functionality includes motif finding, DataFrame-based serialization, and highly expressive graph queries.

The GraphFrames package is available from Spark Packages.

This notebook demonstrates examples from the GraphFrames User Guide: https://graphframes.github.io/graphframes/docs/_site/user-guide.html.

sc.version // link the right library depending on Spark version of the cluster that's running
// spark version 2.3.0 works with graphframes:graphframes:0.7.0-spark2.3-s_2.11
// spark version 3.0.1 works with graphframes:graphframes:0.8.1-spark3.0-s_2.12
res0: String = 3.0.1

Since databricks.com stopped allowing IFrame embeds we have to open it in a separate window now. The blog is insightful and worth a perusal:

  • https://databricks.com/blog/2016/03/03/introducing-graphframes.html
// we first need to install the library - graphframes as a Spark package - and attach it to our cluster - see note two cells above!
import org.apache.spark.sql._
import org.apache.spark.sql.functions._

import org.graphframes._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._

Creating GraphFrames

Let us try to create an example social network from the blog: * https://databricks.com/blog/2016/03/03/introducing-graphframes.html.

Users can create GraphFrames from vertex and edge DataFrames.

  • Vertex DataFrame: A vertex DataFrame should contain a special column named id which specifies unique IDs for each vertex in the graph.
  • Edge DataFrame: An edge DataFrame should contain two special columns: src (source vertex ID of edge) and dst (destination vertex ID of edge).

Both DataFrames can have arbitrary other columns. Those columns can represent vertex and edge attributes.

In our example, we can use a GraphFrame can store data or properties associated with each vertex and edge.

In our social network, each user might have an age and name, and each connection might have a relationship type.

Create the vertices and edges

// Vertex DataFrame
val v = sqlContext.createDataFrame(List(
  ("a", "Alice", 34),
  ("b", "Bob", 36),
  ("c", "Charlie", 30),
  ("d", "David", 29),
  ("e", "Esther", 32),
  ("f", "Fanny", 36),
  ("g", "Gabby", 60)
)).toDF("id", "name", "age")

// Edge DataFrame
val e = sqlContext.createDataFrame(List(
  ("a", "b", "friend"),
  ("b", "c", "follow"),
  ("c", "b", "follow"),
  ("f", "c", "follow"),
  ("e", "f", "follow"),
  ("e", "d", "friend"),
  ("d", "a", "friend"),
  ("a", "e", "friend")
)).toDF("src", "dst", "relationship")
v: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field]
e: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]

Let's create a graph from these vertices and these edges:

val g = GraphFrame(v, e)
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])

Let's use the d3.graphs to visualise graphs (recall the D3 graphs in wiki-click example). You need the Run Cell below using that cell's Play button's drop-down menu.

Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3.graphs.help()

Produces a force-directed graph given a collection of edges of the following form:
case class Edge(src: String, dest: String, count: Long)

Usage:
import d3._
graphs.force(
  height = 500,
  width = 500,
  clicks: Dataset[Edge])

import org.apache.spark.sql.functions.lit // import the lit function in sql
val gE= g.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")) // for us the column count is just an edge incidence
import org.apache.spark.sql.functions.lit
gE: org.apache.spark.sql.DataFrame = [src: string, dest: string ... 1 more field]
display(gE)
src dest count
a b 1.0
b c 1.0
c b 1.0
f c 1.0
e f 1.0
e d 1.0
d a 1.0
a e 1.0
d3.graphs.force(
  height = 500,
  width = 500,
  clicks = gE.as[d3.Edge])

// This example graph also comes with the GraphFrames package.
val g0 = examples.Graphs.friends
g0: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
d3.graphs.force( // let us see g0 now in one cell
  height = 500,
  width = 500,
  clicks = g0.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])

Basic graph and DataFrame queries

GraphFrames provide several simple graph queries, such as node degree.

Also, since GraphFrames represent graphs as pairs of vertex and edge DataFrames, it is easy to make powerful queries directly on the vertex and edge DataFrames. Those DataFrames are made available as vertices and edges fields in the GraphFrame.

Simple queries are simple

GraphFrames make it easy to express queries over graphs. Since GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries.

display(g.vertices)
id name age
a Alice 34.0
b Bob 36.0
c Charlie 30.0
d David 29.0
e Esther 32.0
f Fanny 36.0
g Gabby 60.0
display(g0.vertices) // this is the same query on the graph loaded as an example from GraphFrame package
id name age
a Alice 34.0
b Bob 36.0
c Charlie 30.0
d David 29.0
e Esther 32.0
f Fanny 36.0
g Gabby 60.0
display(g.edges)
src dst relationship
a b friend
b c follow
c b follow
f c follow
e f follow
e d friend
d a friend
a e friend

The incoming degree of the vertices:

display(g.inDegrees)
id inDegree
c 2.0
d 1.0
e 1.0
b 2.0
f 1.0
a 1.0

The outgoing degree of the vertices:

display(g.outDegrees)
id outDegree
a 2.0
c 1.0
e 2.0
d 1.0
b 1.0
f 1.0

The degree of the vertices:

display(g.degrees)
id degree
b 3.0
a 3.0
c 3.0
f 2.0
e 3.0
d 2.0

You can run queries directly on the vertices DataFrame. For example, we can find the age of the youngest person in the graph:

val youngest = g.vertices.groupBy().min("age")
display(youngest)
min(age)
29.0

Likewise, you can run queries on the edges DataFrame.

For example, let us count the number of 'follow' relationships in the graph:

val numFollows = g.edges.filter("relationship = 'follow'").count()
numFollows: Long = 4

Motif finding

More complex relationships involving edges and vertices can be built using motifs.

The following cell finds the pairs of vertices with edges in both directions between them.

The result is a dataframe, in which the column names are given by the motif keys.

Check out the GraphFrame User Guide at https://graphframes.github.io/graphframes/docs/_site/user-guide.html for more details on the API.

// Search for pairs of vertices with edges in both directions between them, i.e., find undirected or bidirected edges.
val motifs = g.find("(a)-[e1]->(b); (b)-[e2]->(a)")
display(motifs)

Since the result is a DataFrame, more complex queries can be built on top of the motif.

Let us find all the reciprocal relationships in which one person is older than 30:

val filtered = motifs.filter("b.age > 30")
display(filtered)

You Try!

//Search for all "directed triangles" or triplets of vertices: a,b,c with edges: a->b, b->c and c->a
//uncomment the next 2 lines and replace the "XXX" below
//val motifs3 = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (c)-[e3]->(XXX)")
//display(motifs3)

Stateful queries

Many motif queries are stateless and simple to express, as in the examples above. The next examples demonstrate more complex queries which carry state along a path in the motif. These queries can be expressed by combining GraphFrame motif finding with filters on the result, where the filters use sequence operations to construct a series of DataFrame Columns.

For example, suppose one wishes to identify a chain of 4 vertices with some property defined by a sequence of functions. That is, among chains of 4 vertices a->b->c->d, identify the subset of chains matching this complex filter:

  • Initialize state on path.
  • Update state based on vertex a.
  • Update state based on vertex b.
  • Etc. for c and d.
  • If final state matches some condition, then the chain is accepted by the filter.

The below code snippets demonstrate this process, where we identify chains of 4 vertices such that at least 2 of the 3 edges are friend relationships. In this example, the state is the current count of friend edges; in general, it could be any DataFrame Column.

// Find chains of 4 vertices.
val chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")

// Query on sequence, with state (cnt)
//  (a) Define method for updating state given the next element of the motif.
def sumFriends(cnt: Column, relationship: Column): Column = {
  when(relationship === "friend", cnt + 1).otherwise(cnt)
}
//  (b) Use sequence operation to apply method to sequence of elements in motif.
//      In this case, the elements are the 3 edges.
val condition = Seq("ab", "bc", "cd").
  foldLeft(lit(0))((cnt, e) => sumFriends(cnt, col(e)("relationship")))
//  (c) Apply filter to DataFrame.
val chainWith2Friends2 = chain4.where(condition >= 2)
display(chainWith2Friends2)
chain4
res22: org.apache.spark.sql.DataFrame = [a: struct<id: string, name: string ... 1 more field>, ab: struct<src: string, dst: string ... 1 more field> ... 5 more fields]
chain4.printSchema
root
 |-- a: struct (nullable = false)
 |    |-- id: string (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- age: integer (nullable = false)
 |-- ab: struct (nullable = false)
 |    |-- src: string (nullable = true)
 |    |-- dst: string (nullable = true)
 |    |-- relationship: string (nullable = true)
 |-- b: struct (nullable = false)
 |    |-- id: string (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- age: integer (nullable = false)
 |-- bc: struct (nullable = false)
 |    |-- src: string (nullable = true)
 |    |-- dst: string (nullable = true)
 |    |-- relationship: string (nullable = true)
 |-- c: struct (nullable = false)
 |    |-- id: string (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- age: integer (nullable = false)
 |-- cd: struct (nullable = false)
 |    |-- src: string (nullable = true)
 |    |-- dst: string (nullable = true)
 |    |-- relationship: string (nullable = true)
 |-- d: struct (nullable = false)
 |    |-- id: string (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- age: integer (nullable = false)

An idea -- a diatribe into an AI security product.

Can you think of a way to use stateful queries in social media networks to find perpetrators of hate-speech online who are possibly worthy of an investigation by domain experts, say in the intelligence or security domain, for potential prosecution on charges of having incited another person to cause physical violence... This is a real problem today as Swedish law effectively prohibits certain forms of online hate-speech.

An idea for a product that can be used by Swedish security agencies?

See https://näthatsgranskaren.se/ for details of a non-profit in Sweden doing such operaitons mostly manually as of early 2020.

Subgraphs

Subgraphs are built by filtering a subset of edges and vertices. For example, the following subgraph only contains people who are friends and who are more than 30 years old.

// Select subgraph of users older than 30, and edges of type "friend"
val v2 = g.vertices.filter("age > 30")
val e2 = g.edges.filter("relationship = 'friend'")
val g2 = GraphFrame(v2, e2)
v2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, name: string ... 1 more field]
e2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
id name age
a Alice 34.0
b Bob 36.0
e Esther 32.0
f Fanny 36.0
g Gabby 60.0
display(g2.edges)
src dst relationship
a b friend
e d friend
d a friend
a e friend
d3.graphs.force( // let us see g2 now in one cell
  height = 500,
  width = 500,
  clicks = g2.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])

Complex triplet filters

The following example shows how to select a subgraph based upon triplet filters which operate on:

  • an edge and
  • its src and
  • dst vertices.

This example could be extended to go beyond triplets by using more complex motifs.

// Select subgraph based on edges "e" of type "follow"
// pointing from a younger user "a" to an older user "b".
val paths = g.find("(a)-[e]->(b)")
  .filter("e.relationship = 'follow'")
  .filter("a.age < b.age")
// "paths" contains vertex info. Extract the edges.
val e2 = paths.select("e.src", "e.dst", "e.relationship")
// In Spark 1.5+, the user may simplify this call:
//  val e2 = paths.select("e.*")

// Construct the subgraph
val g2 = GraphFrame(g.vertices, e2)
paths: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: string, name: string ... 1 more field>, e: struct<src: string, dst: string ... 1 more field> ... 1 more field]
e2: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
id name age
a Alice 34.0
b Bob 36.0
c Charlie 30.0
d David 29.0
e Esther 32.0
f Fanny 36.0
g Gabby 60.0
display(g2.edges)
src dst relationship
c b follow
e f follow

Standard graph algorithms in GraphX conveniently via GraphFrames

GraphFrames comes with a number of standard graph algorithms built in:

  • Breadth-first search (BFS)
  • Connected components
  • Strongly connected components
  • Label Propagation Algorithm (LPA)
  • PageRank
  • Shortest paths
  • Triangle count

Read

https://graphframes.github.io/graphframes/docs/_site/user-guide.html

Search from "Esther" for users of age < 32.

// Search from "Esther" for users of age <= 32.
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32").run()
display(paths)
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther' OR name = 'Bob'").toExpr("age < 32").run()
display(paths)

The search may also be limited by edge filters and maximum path lengths.

val filteredPaths = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
  .edgeFilter("relationship != 'friend'")
  .maxPathLength(3)
  .run()
display(filteredPaths)

Connected components

Compute the connected component membership of each vertex and return a graph with each vertex assigned a component ID.

READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components.

From https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components:-

NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm requires setting a Spark checkpoint directory. Users can revert to the old algorithm using .setAlgorithm("graphx").

Recall the following quote from Chapter 5 on Effective Transformations of the High Performance Spark Book why one needs to check-point to keep the RDD lineage DAGs from growing too large.

Types of Reuse: Cache, Persist, Checkpoint, Shuffle Files If you decide that you need to reuse your RDD, Spark provides a multitude of options for how to store the RDD. Thus it is important to understand when to use the various types of persistence.There are three primary operations that you can use to store your RDD: cache, persist, and checkpoint. In general, caching (equivalent to persisting with the in-memory storage) and persisting are most useful to avoid recomputation during one Spark job or to break RDDs with long lineages, since they keep an RDD on the executors during a Spark job. Checkpointing is most useful to prevent failures and a high cost of recomputation by saving intermediate results. Like persisting, checkpointing helps avoid computation, thus minimizing the cost of failure, and avoids recomputation by breaking the lineage graph.

sc.setCheckpointDir("/_checkpoint") // just a directory in distributed file system
val result = g.connectedComponents.run() 
display(result)
id name age component
a Alice 34.0 4.12316860416e11
b Bob 36.0 4.12316860416e11
c Charlie 30.0 4.12316860416e11
d David 29.0 4.12316860416e11
e Esther 32.0 4.12316860416e11
f Fanny 36.0 4.12316860416e11
g Gabby 60.0 1.46028888064e11

Fun Exercise: Try to modify the d3.graph function to allow a visualisation of a given Sequence of component ids in the above result.

Strongly connected components

Compute the strongly connected component (SCC) of each vertex and return a graph with each vertex assigned to the SCC containing that vertex.

READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#strongly-connected-components.

val result = g.stronglyConnectedComponents.maxIter(10).run()
display(result.orderBy("component"))
id name age component
g Gabby 60.0 1.46028888064e11
f Fanny 36.0 4.12316860416e11
a Alice 34.0 6.70014898176e11
e Esther 32.0 6.70014898176e11
d David 29.0 6.70014898176e11
b Bob 36.0 1.047972020224e12
c Charlie 30.0 1.047972020224e12

Label propagation

Run static Label Propagation Algorithm for detecting communities in networks.

Each node in the network is initially assigned to its own community. At every superstep, nodes send their community affiliation to all neighbors and update their state to the mode community affiliation of incoming messages.

LPA is a standard community detection algorithm for graphs. It is very inexpensive computationally, although

  • (1) convergence is not guaranteed and
  • (2) one can end up with trivial solutions (all nodes are identified into a single community).

READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#label-propagation-algorithm-lpa.

val result = g.labelPropagation.maxIter(5).run()
display(result.orderBy("label"))
id name age label
g Gabby 60.0 1.46028888064e11
b Bob 36.0 1.047972020224e12
e Esther 32.0 1.382979469312e12
a Alice 34.0 1.382979469312e12
c Charlie 30.0 1.382979469312e12
f Fanny 36.0 1.46028888064e12
d David 29.0 1.46028888064e12

PageRank

Identify important vertices in a graph based on connections.

READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#pagerank.

// Run PageRank until convergence to tolerance "tol".
val results = g.pageRank.resetProbability(0.15).tol(0.01).run()
display(results.vertices)
id name age pagerank
b Bob 36.0 2.655507832863289
e Esther 32.0 0.37085233187676075
a Alice 34.0 0.44910633706538744
f Fanny 36.0 0.3283606792049851
g Gabby 60.0 0.1799821386239711
d David 29.0 0.3283606792049851
c Charlie 30.0 2.6878300011606218
display(results.edges)
src dst relationship weight
f c follow 1.0
e f follow 0.5
e d friend 0.5
d a friend 1.0
c b follow 1.0
b c follow 1.0
a e friend 0.5
a b friend 0.5
// Run PageRank for a fixed number of iterations.
val results2 = g.pageRank.resetProbability(0.15).maxIter(10).run()
display(results2.vertices)
id name age pagerank
b Bob 36.0 2.7025217677349773
e Esther 32.0 0.3613490987992571
a Alice 34.0 0.4485115093698443
f Fanny 36.0 0.32504910549694244
g Gabby 60.0 0.17073170731707318
d David 29.0 0.32504910549694244
c Charlie 30.0 2.6667877057849627
// Run PageRank personalized for vertex "a"
val results3 = g.pageRank.resetProbability(0.15).maxIter(10).sourceId("a").run()
display(results3.vertices)
id name age pagerank
b Bob 36.0 0.3366143039702568
e Esther 32.0 7.657840357273027e-2
a Alice 34.0 0.17710831642683564
f Fanny 36.0 3.189213697274781e-2
g Gabby 60.0 0.0
d David 29.0 3.189213697274781e-2
c Charlie 30.0 0.3459147020846817

Shortest paths

Computes shortest paths to the given set of landmark vertices, where landmarks are specified by vertex ID.

READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#shortest-paths.

val paths = g.shortestPaths.landmarks(Seq("a", "d")).run()
display(paths)
g.edges.show()
+---+---+------------+
|src|dst|relationship|
+---+---+------------+
|  a|  b|      friend|
|  b|  c|      follow|
|  c|  b|      follow|
|  f|  c|      follow|
|  e|  f|      follow|
|  e|  d|      friend|
|  d|  a|      friend|
|  a|  e|      friend|
+---+---+------------+

Triangle count

Computes the number of triangles passing through each vertex.

val results = g.triangleCount.run()
display(results)
count id name age
1.0 a Alice 34.0
0.0 b Bob 36.0
0.0 c Charlie 30.0
1.0 d David 29.0
1.0 e Esther 32.0
0.0 f Fanny 36.0
0.0 g Gabby 60.0

YouTry

Read about https://graphframes.github.io/graphframes/docs/_site/user-guide.html#message-passing-via-aggregatemessages

and undestand how the below code snippet shows how to use aggregateMessages to compute the sum of the ages of adjacent users.

import org.graphframes.{examples,GraphFrame}
import org.graphframes.lib.AggregateMessages
val g: GraphFrame = examples.Graphs.friends  // get example graph

// We will use AggregateMessages utilities later, so name it "AM" for short.
val AM = AggregateMessages

// For each user, sum the ages of the adjacent users.
val msgToSrc = AM.dst("age")
val msgToDst = AM.src("age")
val agg = { g.aggregateMessages
  .sendToSrc(msgToSrc)  // send destination user's age to source
  .sendToDst(msgToDst)  // send source user's age to destination
  .agg(sum(AM.msg).as("summedAges")) } // sum up ages, stored in AM.msg column
agg.show()
+---+----------+
| id|summedAges|
+---+----------+
|  a|        97|
|  c|       108|
|  e|        99|
|  d|        66|
|  b|        94|
|  f|        62|
+---+----------+

import org.graphframes.{examples, GraphFrame}
import org.graphframes.lib.AggregateMessages
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
AM: org.graphframes.lib.AggregateMessages.type = org.graphframes.lib.AggregateMessages$@706833c8
msgToSrc: org.apache.spark.sql.Column = dst[age]
msgToDst: org.apache.spark.sql.Column = src[age]
agg: org.apache.spark.sql.DataFrame = [id: string, summedAges: bigint]

There is a lot more that can be done with aggregate messaging - let's get into belief propogation algorithm for a more complex example!

Belief propogation is a powerful computational framework for Graphical Models.

as

This provides a template for building customized BP algorithms for different types of graphical models.

Project Idea

Understand parallel belief propagation using colored fields in the Scala code linked above and also pasted below in one cell (for you to modify if you want to do it in a databricks or jupyter or zeppelin notebook) unless you want to fork and extend the github repo directly with your own example.

Then use it with necessary adaptations to be able to model your favorite interacting particle system. Don't just redo the Ising model done there!

This can be used to gain intuition for various real-world scenarios, including the mathematics in your head:

  • Make a graph for contact network of a set of hosts
  • A simple model of COVID spreading in an SI or SIS or SIR or other epidemic models
    • this can be abstract and simply show your skills in programming, say create a random network
    • or be more explicit with some assumptions about the contact process (population sampled, in one or two cities, with some assumptions on contacts during transportation, school, work, etc)
    • show that you have a fully scalable simulation model that can theoretically scale to billions of hosts

The project does not have to be a recommendation to Swedish authorities! Just a start in the right direction, for instance.

Some readings that can help here include the following and references therein:

  • The Transmission Process: A Combinatorial Stochastic Process for the Evolution of Transmission Trees over Networks, Raazesh Sainudiin and David Welch, Journal of Theoretical Biology, Volume 410, Pages 137–170, 10.1016/j.jtbi.2016.07.038, 2016.

Other Project Ideas

  • try to do a scalable inference algorithm for one of the graphical models that you already know...
  • make a large simulaiton of your favourite Finite Markov Information Exchange (FMIE) process defined by Aldous (see reference in the above linked paper)
  • anything else that fancies you or your research orientation/interests and can benefit from adapting the template for the parallel belief propagation algorithm here.

If you want to do this project in databricks (or other) notebook then start by modifying the following code from the example and making it run... Then adapt... start in small steps... make a team with fellow students with complementary skills...

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.graphframes.examples

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{Graph, VertexRDD, Edge => GXEdge}
import org.apache.spark.sql.{Column, Row, SparkSession, SQLContext}
import org.apache.spark.sql.functions.{col, lit, sum, udf, when}

import org.graphframes.GraphFrame
import org.graphframes.examples.Graphs.gridIsingModel
import org.graphframes.lib.AggregateMessages


/**
 * Example code for Belief Propagation (BP)
 *
 * This provides a template for building customized BP algorithms for different types of
 * graphical models.
 *
 * This example:
 *  - Ising model on a grid
 *  - Parallel Belief Propagation using colored fields
 *
 * Ising models are probabilistic graphical models over binary variables x,,i,,.
 * Each binary variable x,,i,, corresponds to one vertex, and it may take values -1 or +1.
 * The probability distribution P(X) (over all x,,i,,) is parameterized by vertex factors a,,i,,
 * and edge factors b,,ij,,:
 * {{{
 *  P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]
 * }}}
 * where Z is the normalization constant (partition function).
 * See [[https://en.wikipedia.org/wiki/Ising_model Wikipedia]] for more information on Ising models.
 *
 * Belief Propagation (BP) provides marginal probabilities of the values of the variables x,,i,,,
 * i.e., P(x,,i,,) for each i.  This allows a user to understand likely values of variables.
 * See [[https://en.wikipedia.org/wiki/Belief_propagation Wikipedia]] for more information on BP.
 *
 * We use a batch synchronous BP algorithm, where batches of vertices are updated synchronously.
 * We follow the mean field update algorithm in Slide 13 of the
 * [[http://www.eecs.berkeley.edu/~wainwrig/Talks/A_GraphModel_Tutorial  talk slides]] from:
 *  Wainwright. "Graphical models, message-passing algorithms, and convex optimization."
 *
 * The batches are chosen according to a coloring.  For background on graph colorings for inference,
 * see for example:
 *  Gonzalez et al. "Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees."
 *  AISTATS, 2011.
 *
 * The BP algorithm works by:
 *  - Coloring the graph by assigning a color to each vertex such that no neighboring vertices
 *    share the same color.
 *  - In each step of BP, update all vertices of a single color.  Alternate colors.
 */
object BeliefPropagation {

  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder()
      .appName("BeliefPropagation example")
      .getOrCreate()

    val sql = spark.sqlContext

    // Create graphical model g of size 3 x 3.
    val g = gridIsingModel(sql, 3)

    println("Original Ising model:")
    g.vertices.show()
    g.edges.show()

    // Run BP for 5 iterations.
    val numIter = 5
    val results = runBPwithGraphX(g, numIter)

    // Display beliefs.
    val beliefs = results.vertices.select("id", "belief")
    println(s"Done with BP. Final beliefs after $numIter iterations:")
    beliefs.show()

    spark.stop()
  }

  /**
   * Given a GraphFrame, choose colors for each vertex.  No neighboring vertices will share the
   * same color.  The number of colors is minimized.
   *
   * This is written specifically for grid graphs. For non-grid graphs, it should be generalized,
   * such as by using a greedy coloring scheme.
   *
   * @param g  Grid graph generated by [[org.graphframes.examples.Graphs.gridIsingModel()]]
   * @return  Same graph, but with a new vertex column "color" of type Int (0 or 1)
   */
  private def colorGraph(g: GraphFrame): GraphFrame = {
    val colorUDF = udf { (i: Int, j: Int) => (i + j) % 2 }
    val v = g.vertices.withColumn("color", colorUDF(col("i"), col("j")))
    GraphFrame(v, g.edges)
  }

  /**
   * Run Belief Propagation.
   *
   * This implementation of BP shows how to use GraphX's aggregateMessages method.
   * It is simple to convert to and from GraphX format.  This method does the following:
   *  - Color GraphFrame vertices for BP scheduling.
   *  - Convert GraphFrame to GraphX format.
   *  - Run BP using GraphX's aggregateMessages API.
   *  - Augment the original GraphFrame with the BP results (vertex beliefs).
   *
   * @param g  Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
   * @param numIter  Number of iterations of BP to run.  One iteration includes updating each
   *                 vertex's belief once.
   * @return  Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
   *          "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
   *          value +1 instead of -1.
   */
  def runBPwithGraphX(g: GraphFrame, numIter: Int): GraphFrame = {
    // Choose colors for vertices for BP scheduling.
    val colorG = colorGraph(g)
    val numColors: Int = colorG.vertices.select("color").distinct.count().toInt

    // Convert GraphFrame to GraphX, and initialize beliefs.
    val gx0 = colorG.toGraphX
    // Schema maps for extracting attributes
    val vColsMap = colorG.vertexColumnMap
    val eColsMap = colorG.edgeColumnMap
    // Convert vertex attributes to nice case classes.
    val gx1: Graph[VertexAttr, Row] = gx0.mapVertices { case (_, attr) =>
      // Initialize belief at 0.0
      VertexAttr(attr.getDouble(vColsMap("a")), 0.0, attr.getInt(vColsMap("color")))
    }
    // Convert edge attributes to nice case classes.
    val extractEdgeAttr: (GXEdge[Row] => EdgeAttr) = { e =>
      EdgeAttr(e.attr.getDouble(eColsMap("b")))
    }
    var gx: Graph[VertexAttr, EdgeAttr] = gx1.mapEdges(extractEdgeAttr)

    // Run BP for numIter iterations.
    for (iter <- Range(0, numIter)) {
      // For each color, have that color receive messages from neighbors.
      for (color <- Range(0, numColors)) {
        // Send messages to vertices of the current color.
        val msgs: VertexRDD[Double] = gx.aggregateMessages(
          ctx =>
            // Can send to source or destination since edges are treated as undirected.
            if (ctx.dstAttr.color == color) {
              val msg = ctx.attr.b * ctx.srcAttr.belief
              // Only send message if non-zero.
              if (msg != 0) ctx.sendToDst(msg)
            } else if (ctx.srcAttr.color == color) {
              val msg = ctx.attr.b * ctx.dstAttr.belief
              // Only send message if non-zero.
              if (msg != 0) ctx.sendToSrc(msg)
            },
          _ + _)
        // Receive messages, and update beliefs for vertices of the current color.
        gx = gx.outerJoinVertices(msgs) {
          case (vID, vAttr, optMsg) =>
            if (vAttr.color == color) {
              val x = vAttr.a + optMsg.getOrElse(0.0)
              val newBelief = math.exp(-log1pExp(-x))
              VertexAttr(vAttr.a, newBelief, color)
            } else {
              vAttr
            }
        }
      }
    }

    // Convert back to GraphFrame with a new column "belief" for vertices DataFrame.
    val gxFinal: Graph[Double, Unit] = gx.mapVertices((_, attr) => attr.belief).mapEdges(_ => ())
    GraphFrame.fromGraphX(colorG, gxFinal, vertexNames = Seq("belief"))
  }

  case class VertexAttr(a: Double, belief: Double, color: Int)

  case class EdgeAttr(b: Double)

  /**
   * Run Belief Propagation.
   *
   * This implementation of BP shows how to use GraphFrame's aggregateMessages method.
   *  - Color GraphFrame vertices for BP scheduling.
   *  - Run BP using GraphFrame's aggregateMessages API.
   *  - Augment the original GraphFrame with the BP results (vertex beliefs).
   *
   * @param g  Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
   * @param numIter  Number of iterations of BP to run.  One iteration includes updating each
   *                 vertex's belief once.
   * @return  Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
   *          "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
   *          value +1 instead of -1.
   */
  def runBPwithGraphFrames(g: GraphFrame, numIter: Int): GraphFrame = {
    // Choose colors for vertices for BP scheduling.
    val colorG = colorGraph(g)
    val numColors: Int = colorG.vertices.select("color").distinct.count().toInt

    // TODO: Handle vertices without any edges.

    // Initialize vertex beliefs at 0.0.
    var gx = GraphFrame(colorG.vertices.withColumn("belief", lit(0.0)), colorG.edges)

    // Run BP for numIter iterations.
    for (iter <- Range(0, numIter)) {
      // For each color, have that color receive messages from neighbors.
      for (color <- Range(0, numColors)) {
        // Define "AM" for shorthand for referring to the src, dst, edge, and msg fields.
        // (See usage below.)
        val AM = AggregateMessages
        // Send messages to vertices of the current color.
        // We may send to source or destination since edges are treated as undirected.
        val msgForSrc: Column = when(AM.src("color") === color, AM.edge("b") * AM.dst("belief"))
        val msgForDst: Column = when(AM.dst("color") === color, AM.edge("b") * AM.src("belief"))
        val logistic = udf { (x: Double) => math.exp(-log1pExp(-x)) }
        val aggregates = gx.aggregateMessages
          .sendToSrc(msgForSrc)
          .sendToDst(msgForDst)
          .agg(sum(AM.msg).as("aggMess"))
        val v = gx.vertices
        // Receive messages, and update beliefs for vertices of the current color.
        val newBeliefCol = when(v("color") === color && aggregates("aggMess").isNotNull,
          logistic(aggregates("aggMess") + v("a")))
          .otherwise(v("belief"))  // keep old beliefs for other colors
        val newVertices = v
          .join(aggregates, v("id") === aggregates("id"), "left_outer")  // join messages, vertices
          .drop(aggregates("id"))  // drop duplicate ID column (from outer join)
          .withColumn("newBelief", newBeliefCol)  // compute new beliefs
          .drop("aggMess")  // drop messages
          .drop("belief")  // drop old beliefs
          .withColumnRenamed("newBelief", "belief")
        // Cache new vertices using workaround for SPARK-13346
        val cachedNewVertices = AM.getCachedDataFrame(newVertices)
        gx = GraphFrame(cachedNewVertices, gx.edges)
      }
    }

    // Drop the "color" column from vertices
    GraphFrame(gx.vertices.drop("color"), gx.edges)
  }

  /** More numerically stable `log(1 + exp(x))` */
  private def log1pExp(x: Double): Double = {
    if (x > 0) {
      x + math.log1p(math.exp(-x))
    } else {
      math.log1p(math.exp(x))
    }
  }
}

ScaDaMaLe Course site and book

This is a scala version of the python notebook in the following talk:

Homework:

See https://www.brighttalk.com/webcast/12891/199003 (you need to subscribe freely to Bright Talk first). Then go through this scala version of the notebook from the talk.

On-Time Flight Performance with GraphFrames for Apache Spark

This notebook provides an analysis of On-Time Flight Performance and Departure Delays data using GraphFrames for Apache Spark.

Source Data:

References:

Preparation

Extract the Airports and Departure Delays information from S3 / DBFS

// Set File Paths
val tripdelaysFilePath = "/databricks-datasets/flights/departuredelays.csv"
val airportsnaFilePath = "/databricks-datasets/flights/airport-codes-na.txt"
tripdelaysFilePath: String = /databricks-datasets/flights/departuredelays.csv
airportsnaFilePath: String = /databricks-datasets/flights/airport-codes-na.txt
// Obtain airports dataset
// Note that "spark-csv" package is built-in datasource in Spark 2.0
val airportsna = sqlContext.read.format("com.databricks.spark.csv").
  option("header", "true").
  option("inferschema", "true").
  option("delimiter", "\t").
  load(airportsnaFilePath)

airportsna.createOrReplaceTempView("airports_na")

// Obtain departure Delays data
val departureDelays = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load(tripdelaysFilePath)
departureDelays.createOrReplaceTempView("departureDelays")
departureDelays.cache()

// Available IATA (International Air Transport Association) codes from the departuredelays sample dataset
val tripIATA = sqlContext.sql("select distinct iata from (select distinct origin as iata from departureDelays union all select distinct destination as iata from departureDelays) a")
tripIATA.createOrReplaceTempView("tripIATA")

// Only include airports with atleast one trip from the departureDelays dataset
val airports = sqlContext.sql("select f.IATA, f.City, f.State, f.Country from airports_na f join tripIATA t on t.IATA = f.IATA")
airports.createOrReplaceTempView("airports")
airports.cache()
airportsna: org.apache.spark.sql.DataFrame = [City: string, State: string ... 2 more fields]
departureDelays: org.apache.spark.sql.DataFrame = [date: string, delay: string ... 3 more fields]
tripIATA: org.apache.spark.sql.DataFrame = [iata: string]
airports: org.apache.spark.sql.DataFrame = [IATA: string, City: string ... 2 more fields]
res0: airports.type = [IATA: string, City: string ... 2 more fields]
// Build `departureDelays_geo` DataFrame
// Obtain key attributes such as Date of flight, delays, distance, and airport information (Origin, Destination)  
val departureDelays_geo = sqlContext.sql("select cast(f.date as int) as tripid, cast(concat(concat(concat(concat(concat(concat('2014-', concat(concat(substr(cast(f.date as string), 1, 2), '-')), substr(cast(f.date as string), 3, 2)), ' '), substr(cast(f.date as string), 5, 2)), ':'), substr(cast(f.date as string), 7, 2)), ':00') as timestamp) as `localdate`, cast(f.delay as int), cast(f.distance as int), f.origin as src, f.destination as dst, o.city as city_src, d.city as city_dst, o.state as state_src, d.state as state_dst from departuredelays f join airports o on o.iata = f.origin join airports d on d.iata = f.destination") 

// RegisterTempTable
departureDelays_geo.createOrReplaceTempView("departureDelays_geo")

// Cache and Count
departureDelays_geo.cache()
departureDelays_geo.count()
departureDelays_geo: org.apache.spark.sql.DataFrame = [tripid: int, localdate: timestamp ... 8 more fields]
res2: Long = 1361141
display(departureDelays_geo)
tripid localdate delay distance src dst city_src city_dst state_src state_dst
1011111.0 2014-01-01T11:11:00.000+0000 -5.0 221.0 MSP INL Minneapolis International Falls MN MN
1021111.0 2014-01-02T11:11:00.000+0000 7.0 221.0 MSP INL Minneapolis International Falls MN MN
1031111.0 2014-01-03T11:11:00.000+0000 0.0 221.0 MSP INL Minneapolis International Falls MN MN
1041925.0 2014-01-04T19:25:00.000+0000 0.0 221.0 MSP INL Minneapolis International Falls MN MN
1061115.0 2014-01-06T11:15:00.000+0000 33.0 221.0 MSP INL Minneapolis International Falls MN MN
1071115.0 2014-01-07T11:15:00.000+0000 23.0 221.0 MSP INL Minneapolis International Falls MN MN
1081115.0 2014-01-08T11:15:00.000+0000 -9.0 221.0 MSP INL Minneapolis International Falls MN MN
1091115.0 2014-01-09T11:15:00.000+0000 11.0 221.0 MSP INL Minneapolis International Falls MN MN
1101115.0 2014-01-10T11:15:00.000+0000 -3.0 221.0 MSP INL Minneapolis International Falls MN MN
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Building the Graph

Now that we've imported our data, we're going to need to build our graph. To do so we're going to do two things. We are going to build the structure of the vertices (or nodes) and we're going to build the structure of the edges. What's awesome about GraphFrames is that this process is incredibly simple.

  • Rename IATA airport code to id in the Vertices Table
  • Start and End airports to src and dst for the Edges Table (flights)

These are required naming conventions for vertices and edges in GraphFrames.

WARNING: If the graphframes package, required in the cell below, is not installed, follow the instructions here.

// Note, ensure you have already installed the GraphFrames spack-package
import org.apache.spark.sql.functions._
import org.graphframes._

// Create Vertices (airports) and Edges (flights)
val tripVertices = airports.withColumnRenamed("IATA", "id").distinct()
val tripEdges = departureDelays_geo.select("tripid", "delay", "src", "dst", "city_dst", "state_dst")

// Cache Vertices and Edges
tripEdges.cache()
tripVertices.cache()
import org.apache.spark.sql.functions._
import org.graphframes._
tripVertices: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, City: string ... 2 more fields]
tripEdges: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 4 more fields]
res5: tripVertices.type = [id: string, City: string ... 2 more fields]
// Vertices
// The vertices of our graph are the airports
display(tripVertices)
id City State Country
FAT Fresno CA USA
CMH Columbus OH USA
PHX Phoenix AZ USA
PAH Paducah KY USA
COS Colorado Springs CO USA
RNO Reno NV USA
MYR Myrtle Beach SC USA
VLD Valdosta GA USA
BPT Beaumont TX USA
CAE Columbia SC USA
PSC Pasco WA USA
SRQ Sarasota FL USA
LAX Los Angeles CA USA
DAY Dayton OH USA
AVP Wilkes-Barre PA USA
MFR Medford OR USA
JFK New York NY USA
BNA Nashville TN USA
CLT Charlotte NC USA
LAS Las Vegas NV USA
BDL Hartford CT USA
ILG Wilmington DE USA
ACT Waco TX USA
ATW Appleton WI USA
RHI Rhinelander WI USA
PWM Portland ME USA
SJT San Angelo TX USA
APN Alpena MI USA
GRB Green Bay WI USA
CAK Akron OH USA
LAN Lansing MI USA
FLL Fort Lauderdale FL USA
MSY New Orleans LA USA
SAT San Antonio TX USA
TPA Tampa FL USA
COD Cody WY USA
MOD Modesto CA USA
GTR Columbus MS USA
BTV Burlington VT USA
RDD Redding CA USA
HLN Helena MT USA
CPR Casper WY USA
FWA Fort Wayne IN USA
IMT Iron Mountain MI USA
DTW Detroit MI USA
BZN Bozeman MT USA
SBN South Bend IN USA
SPS Wichita Falls TX USA
BFL Bakersfield CA USA
HOB Hobbs NM USA
TVC Traverse City MI USA
CLE Cleveland OH USA
ABR Aberdeen SD USA
CHS Charleston SC USA
GUC Gunnison CO USA
IND Indianapolis IN USA
SDF Louisville KY USA
SAN San Diego CA USA
RSW Fort Myers FL USA
BOS Boston MA USA
TUL Tulsa OK USA
AGS Augusta GA USA
MOB Mobile AL USA
TUS Tucson AZ USA
KTN Ketchikan AK USA
BTR Baton Rouge LA USA
PNS Pensacola FL USA
ABQ Albuquerque NM USA
LGA New York NY USA
DAL Dallas TX USA
JNU Juneau AK USA
MAF Midland TX USA
RIC Richmond VA USA
FAR Fargo ND USA
MTJ Montrose CO USA
AMA Amarillo TX USA
ROC Rochester NY USA
SHV Shreveport LA USA
YAK Yakutat AK USA
CSG Columbus GA USA
ALO Waterloo IA USA
DRO Durango CO USA
CRP Corpus Christi TX USA
FNT Flint MI USA
GSO Greensboro NC USA
LWS Lewiston ID USA
TOL Toledo OH USA
GTF Great Falls MT USA
MKE Milwaukee WI USA
RKS Rock Springs WY USA
STL St. Louis MO USA
MHT Manchester NH USA
CRW Charleston WV USA
SLC Salt Lake City UT USA
ACV Eureka CA USA
DFW Dallas TX USA
OME Nome AK USA
ORF Norfolk VA USA
RDU Raleigh NC USA
SYR Syracuse NY USA
BQK Brunswick GA USA
ROA Roanoke VA USA
OKC Oklahoma City OK USA
EYW Key West FL USA
BOI Boise ID USA
ABY Albany GA USA
PIA Peoria IL USA
GRI Grand Island NE USA
PBI West Palm Beach FL USA
FSM Fort Smith AR USA
TYS Knoxville TN USA
DAB Daytona Beach FL USA
TTN Trenton NJ USA
MDT Harrisburg PA USA
AUS Austin TX USA
DCA Washington DC null USA
PIH Pocatello ID USA
GCC Gillette WY USA
BUR Burbank CA USA
GRK Killeen TX USA
LBB Lubbock TX USA
JAN Jackson MS USA
MSP Minneapolis MN USA
SAF Santa Fe NM USA
HPN White Plains NY USA
DHN Dothan AL USA
PSP Palm Springs CA USA
INL International Falls MN USA
CIC Chico CA USA
EGE Vail CO USA
CLD Carlsbad CA USA
RST Rochester MN USA
DEN Denver CO USA
EUG Eugene OR USA
LFT Lafayette LA USA
PVD Providence RI USA
DBQ Dubuque IA USA
SAV Savannah GA USA
SFO San Francisco CA USA
JAX Jacksonville FL USA
LIT Little Rock AR USA
IDA Idaho Falls ID USA
TYR Tyler TX USA
ANC Anchorage AK USA
HRL Harlingen TX USA
LMT Klamath Falls OR USA
PHF Newport News VA USA
HOU Houston TX USA
SPI Springfield IL USA
MOT Minot ND USA
BTM Butte MT USA
SMF Sacramento CA USA
HIB Hibbing MN USA
ROW Roswell NM USA
MBS Saginaw MI USA
ILM Wilmington NC USA
LGB Long Beach CA USA
IAD Washington DC null USA
ICT Wichita KS USA
BGR Bangor ME USA
ELP El Paso TX USA
SUX Sioux City IA USA
HSV Huntsville AL USA
SIT Sitka AK USA
CWA Wausau WI USA
LCH Lake Charles LA USA
CMI Champaign IL USA
ELM Elmira NY USA
CLL College Station TX USA
VPS Fort Walton Beach FL USA
MSN Madison WI USA
MHK Manhattan KS USA
OAJ Jacksonville NC USA
EKO Elko NV USA
FSD Sioux Falls SD USA
EWR Newark NJ USA
MEM Memphis TN USA
ADQ Kodiak AK USA
GGG Longview TX USA
BLI Bellingham WA USA
OMA Omaha NE USA
ATL Atlanta GA USA
SJC San Jose CA USA
SBA Santa Barbara CA USA
ONT Ontario CA USA
EAU Eau Claire WI USA
GPT Gulfport MS USA
SUN Sun Valley ID USA
CHO Charlottesville VA USA
MSO Missoula MT USA
CMX Hancock MI USA
ORD Chicago IL USA
EVV Evansville IN USA
MLU Monroe LA USA
LAW Lawton OK USA
BRW Barrow AK USA
SBP San Luis Obispo CA USA
LIH Lihue, Kauai HI USA
GJT Grand Junction CO USA
FAI Fairbanks AK USA
BIS Bismarck ND USA
SNA Orange County CA USA
LAR Laramie WY USA
JLN Joplin MO USA
LRD Laredo TX USA
LEX Lexington KY USA
SGU St. George UT USA
MCI Kansas City MO USA
AEX Alexandria LA USA
ISN Williston ND USA
TXK Texarkana AR USA
BIL Billings MT USA
CID Cedar Rapids IA USA
PDX Portland OR USA
ABE Allentown PA USA
DIK Dickinson ND USA
ART Watertown NY USA
GCK Garden City KS USA
BET Bethel AK USA
AVL Asheville NC USA
MCO Orlando FL USA
GSP Greenville SC USA
TWF Twin Falls ID USA
MKG Muskegon MI USA
FAY Fayetteville NC USA
SCE State College PA USA
EWN New Bern NC USA
XNA Fayetteville AR USA
MRY Monterey CA USA
MLB Melbourne FL USA
HNL Honolulu, Oahu HI USA
CVG Cincinnati OH USA
RAP Rapid City SD USA
AZO Kalamazoo MI USA
WRG Wrangell AK USA
ISP Islip NY USA
FLG Flagstaff AZ USA
BHM Birmingham AL USA
ALB Albany NY USA
SEA Seattle WA USA
GRR Grand Rapids MI USA
CHA Chattanooga TN USA
IAH Houston TX USA
SMX Santa Maria CA USA
MDW Chicago IL USA
MQT Marquette MI USA
LNK Lincoln NE USA
RDM Redmond OR USA
DLH Duluth MN USA
DSM Des Moines IA USA
OAK Oakland CA USA
PHL Philadelphia PA USA
FCA Kalispell MT USA
MFE McAllen TX USA
OGG Kahului, Maui HI USA
YUM Yuma AZ USA
BMI Bloomington IL USA
GEG Spokane WA USA
TLH Tallahassee FL USA
LSE La Crosse WI USA
MIA Miami FL USA
BRO Brownsville TX USA
JAC Jackson Hole WY USA
CDV Cordova AK USA
TRI Tri-City Airport TN USA
SWF Newburgh NY USA
MGM Montgomery AL USA
BWI Baltimore MD USA
SGF Springfield MO USA
GFK Grand Forks ND USA
GNV Gainesville FL USA
OTH North Bend OR USA
PIT Pittsburgh PA USA
BJI Bemidji MN USA
ASE Aspen CO USA
BUF Buffalo NY USA
COU Columbia MO USA
ABI Abilene TX USA
MLI Moline IL USA
// Edges
// The edges of our graph are the flights between airports
display(tripEdges)
tripid delay src dst city_dst state_dst
1011111.0 -5.0 MSP INL International Falls MN
1021111.0 7.0 MSP INL International Falls MN
1031111.0 0.0 MSP INL International Falls MN
1041925.0 0.0 MSP INL International Falls MN
1061115.0 33.0 MSP INL International Falls MN
1071115.0 23.0 MSP INL International Falls MN
1081115.0 -9.0 MSP INL International Falls MN
1091115.0 11.0 MSP INL International Falls MN
1101115.0 -3.0 MSP INL International Falls MN
1112015.0 -7.0 MSP INL International Falls MN
1121925.0 -5.0 MSP INL International Falls MN
1131115.0 -3.0 MSP INL International Falls MN
1141115.0 -6.0 MSP INL International Falls MN
1151115.0 -7.0 MSP INL International Falls MN
1161115.0 -3.0 MSP INL International Falls MN
1171115.0 4.0 MSP INL International Falls MN
1182015.0 -5.0 MSP INL International Falls MN
1191925.0 -7.0 MSP INL International Falls MN
1201115.0 -6.0 MSP INL International Falls MN
1211115.0 0.0 MSP INL International Falls MN
1221115.0 -4.0 MSP INL International Falls MN
1231115.0 -4.0 MSP INL International Falls MN
1241115.0 -3.0 MSP INL International Falls MN
1252015.0 -12.0 MSP INL International Falls MN
1261925.0 -5.0 MSP INL International Falls MN
1271115.0 0.0 MSP INL International Falls MN
1281115.0 -8.0 MSP INL International Falls MN
1291115.0 -2.0 MSP INL International Falls MN
1301115.0 0.0 MSP INL International Falls MN
1311115.0 -3.0 MSP INL International Falls MN
2012015.0 -4.0 MSP INL International Falls MN
2022015.0 0.0 MSP INL International Falls MN
2031115.0 -7.0 MSP INL International Falls MN
2041115.0 -6.0 MSP INL International Falls MN
2051115.0 -4.0 MSP INL International Falls MN
2061115.0 -2.0 MSP INL International Falls MN
2071115.0 -15.0 MSP INL International Falls MN
2082015.0 -4.0 MSP INL International Falls MN
2091925.0 1.0 MSP INL International Falls MN
2101115.0 -3.0 MSP INL International Falls MN
2111115.0 -7.0 MSP INL International Falls MN
2121115.0 -2.0 MSP INL International Falls MN
2131115.0 -3.0 MSP INL International Falls MN
2141115.0 -11.0 MSP INL International Falls MN
2152015.0 16.0 MSP INL International Falls MN
2161925.0 169.0 MSP INL International Falls MN
2171115.0 27.0 MSP INL International Falls MN
2181115.0 96.0 MSP INL International Falls MN
2191115.0 -9.0 MSP INL International Falls MN
2201115.0 -6.0 MSP INL International Falls MN
2211115.0 -4.0 MSP INL International Falls MN
2222015.0 -4.0 MSP INL International Falls MN
2231925.0 -3.0 MSP INL International Falls MN
2241115.0 -2.0 MSP INL International Falls MN
2251115.0 -6.0 MSP INL International Falls MN
2261115.0 -8.0 MSP INL International Falls MN
2271115.0 -8.0 MSP INL International Falls MN
2281115.0 5.0 MSP INL International Falls MN
3012015.0 -4.0 MSP INL International Falls MN
3022000.0 0.0 MSP INL International Falls MN
3031115.0 17.0 MSP INL International Falls MN
3041115.0 0.0 MSP INL International Falls MN
3051115.0 -7.0 MSP INL International Falls MN
3061115.0 -8.0 MSP INL International Falls MN
3071115.0 -10.0 MSP INL International Falls MN
3082000.0 -11.0 MSP INL International Falls MN
3092000.0 -9.0 MSP INL International Falls MN
3101115.0 -10.0 MSP INL International Falls MN
3111115.0 -8.0 MSP INL International Falls MN
3121115.0 -6.0 MSP INL International Falls MN
3131115.0 -8.0 MSP INL International Falls MN
3141115.0 -5.0 MSP INL International Falls MN
3152000.0 -11.0 MSP INL International Falls MN
3162000.0 -10.0 MSP INL International Falls MN
3171115.0 25.0 MSP INL International Falls MN
3181115.0 2.0 MSP INL International Falls MN
3191115.0 -5.0 MSP INL International Falls MN
3201115.0 -6.0 MSP INL International Falls MN
3211115.0 0.0 MSP INL International Falls MN
3222000.0 -10.0 MSP INL International Falls MN
3232000.0 -9.0 MSP INL International Falls MN
3241115.0 -9.0 MSP INL International Falls MN
3251115.0 -4.0 MSP INL International Falls MN
3261115.0 -5.0 MSP INL International Falls MN
3271115.0 9.0 MSP INL International Falls MN
3281115.0 -7.0 MSP INL International Falls MN
3292000.0 -19.0 MSP INL International Falls MN
3302000.0 -10.0 MSP INL International Falls MN
3311115.0 -8.0 MSP INL International Falls MN
2011230.0 -3.0 EWR MSY New Orleans LA
2010719.0 -2.0 EWR MSY New Orleans LA
2021230.0 -10.0 EWR MSY New Orleans LA
2020709.0 59.0 EWR MSY New Orleans LA
2021654.0 21.0 EWR MSY New Orleans LA
2030719.0 -6.0 EWR MSY New Orleans LA
2031659.0 0.0 EWR MSY New Orleans LA
2031230.0 0.0 EWR MSY New Orleans LA
2032043.0 0.0 EWR MSY New Orleans LA
2041230.0 24.0 EWR MSY New Orleans LA
2040719.0 168.0 EWR MSY New Orleans LA
2041730.0 88.0 EWR MSY New Orleans LA
2042043.0 106.0 EWR MSY New Orleans LA
2051659.0 0.0 EWR MSY New Orleans LA
2050719.0 0.0 EWR MSY New Orleans LA
2050929.0 0.0 EWR MSY New Orleans LA
2052043.0 46.0 EWR MSY New Orleans LA
2060719.0 -3.0 EWR MSY New Orleans LA
2061659.0 82.0 EWR MSY New Orleans LA
2061230.0 61.0 EWR MSY New Orleans LA
2062043.0 7.0 EWR MSY New Orleans LA
2070719.0 8.0 EWR MSY New Orleans LA
2071659.0 19.0 EWR MSY New Orleans LA
2071230.0 27.0 EWR MSY New Orleans LA
2072048.0 47.0 EWR MSY New Orleans LA
2081230.0 -10.0 EWR MSY New Orleans LA
2080719.0 -1.0 EWR MSY New Orleans LA
2091229.0 95.0 EWR MSY New Orleans LA
2091654.0 -5.0 EWR MSY New Orleans LA
2090709.0 -8.0 EWR MSY New Orleans LA
2092043.0 32.0 EWR MSY New Orleans LA
2100719.0 14.0 EWR MSY New Orleans LA
2101659.0 16.0 EWR MSY New Orleans LA
2101230.0 -4.0 EWR MSY New Orleans LA
2102043.0 -4.0 EWR MSY New Orleans LA
2111230.0 10.0 EWR MSY New Orleans LA
2110719.0 46.0 EWR MSY New Orleans LA
2111730.0 -9.0 EWR MSY New Orleans LA
2112043.0 -2.0 EWR MSY New Orleans LA
2120719.0 1.0 EWR MSY New Orleans LA
2121230.0 -4.0 EWR MSY New Orleans LA
2120929.0 89.0 EWR MSY New Orleans LA
2122043.0 36.0 EWR MSY New Orleans LA
2130738.0 0.0 EWR MSY New Orleans LA
2132041.0 0.0 EWR MSY New Orleans LA
2140729.0 0.0 EWR MSY New Orleans LA
2142041.0 0.0 EWR MSY New Orleans LA
2151206.0 0.0 EWR MSY New Orleans LA
2150659.0 0.0 EWR MSY New Orleans LA
2160705.0 -5.0 EWR MSY New Orleans LA
2170729.0 0.0 EWR MSY New Orleans LA
2172041.0 -7.0 EWR MSY New Orleans LA
2180738.0 0.0 EWR MSY New Orleans LA
2182041.0 36.0 EWR MSY New Orleans LA
2190727.0 15.0 EWR MSY New Orleans LA
2200738.0 -7.0 EWR MSY New Orleans LA
2202041.0 51.0 EWR MSY New Orleans LA
2210729.0 -2.0 EWR MSY New Orleans LA
2212041.0 0.0 EWR MSY New Orleans LA
2221206.0 -8.0 EWR MSY New Orleans LA
2220659.0 8.0 EWR MSY New Orleans LA
2232041.0 8.0 EWR MSY New Orleans LA
2230705.0 -10.0 EWR MSY New Orleans LA
2240729.0 6.0 EWR MSY New Orleans LA
2242041.0 -7.0 EWR MSY New Orleans LA
2250738.0 0.0 EWR MSY New Orleans LA
2260727.0 23.0 EWR MSY New Orleans LA
2262041.0 174.0 EWR MSY New Orleans LA
2270738.0 8.0 EWR MSY New Orleans LA
2272041.0 32.0 EWR MSY New Orleans LA
2280729.0 12.0 EWR MSY New Orleans LA
2282041.0 49.0 EWR MSY New Orleans LA
2281000.0 2.0 EWR MSY New Orleans LA
2051230.0 216.0 EWR MSY New Orleans LA
2131536.0 273.0 EWR MSY New Orleans LA
2141536.0 6.0 EWR MSY New Orleans LA
2151902.0 31.0 EWR MSY New Orleans LA
2151536.0 66.0 EWR MSY New Orleans LA
2162041.0 -4.0 EWR MSY New Orleans LA
2161536.0 7.0 EWR MSY New Orleans LA
2171536.0 3.0 EWR MSY New Orleans LA
2181536.0 26.0 EWR MSY New Orleans LA
2191536.0 -9.0 EWR MSY New Orleans LA
2201536.0 -3.0 EWR MSY New Orleans LA
2211536.0 34.0 EWR MSY New Orleans LA
2221900.0 -2.0 EWR MSY New Orleans LA
2221536.0 65.0 EWR MSY New Orleans LA
2231536.0 -3.0 EWR MSY New Orleans LA
2241536.0 -1.0 EWR MSY New Orleans LA
2251536.0 0.0 EWR MSY New Orleans LA
2261536.0 0.0 EWR MSY New Orleans LA
2271536.0 -4.0 EWR MSY New Orleans LA
2281536.0 8.0 EWR MSY New Orleans LA
2010730.0 -1.0 EWR MSY New Orleans LA
2021815.0 -1.0 EWR MSY New Orleans LA
2031815.0 0.0 EWR MSY New Orleans LA
2041815.0 -4.0 EWR MSY New Orleans LA
2051815.0 -4.0 EWR MSY New Orleans LA
2061815.0 -4.0 EWR MSY New Orleans LA
2071815.0 -5.0 EWR MSY New Orleans LA
2080730.0 -4.0 EWR MSY New Orleans LA
2091815.0 -1.0 EWR MSY New Orleans LA
2101815.0 -5.0 EWR MSY New Orleans LA
2111815.0 4.0 EWR MSY New Orleans LA
2121815.0 64.0 EWR MSY New Orleans LA
2131805.0 0.0 EWR MSY New Orleans LA
2141635.0 52.0 EWR MSY New Orleans LA
2150730.0 0.0 EWR MSY New Orleans LA
2161635.0 21.0 EWR MSY New Orleans LA
2171635.0 23.0 EWR MSY New Orleans LA
2181635.0 -5.0 EWR MSY New Orleans LA
2191635.0 21.0 EWR MSY New Orleans LA
2201635.0 0.0 EWR MSY New Orleans LA
2211635.0 292.0 EWR MSY New Orleans LA
2220730.0 28.0 EWR MSY New Orleans LA
2231635.0 0.0 EWR MSY New Orleans LA
2241635.0 -8.0 EWR MSY New Orleans LA
2251635.0 6.0 EWR MSY New Orleans LA
2261635.0 10.0 EWR MSY New Orleans LA
2271635.0 -1.0 EWR MSY New Orleans LA
2281635.0 -1.0 EWR MSY New Orleans LA
3011206.0 -5.0 EWR MSY New Orleans LA
3010659.0 -1.0 EWR MSY New Orleans LA
3022041.0 0.0 EWR MSY New Orleans LA
3020705.0 -6.0 EWR MSY New Orleans LA
3030729.0 0.0 EWR MSY New Orleans LA
3032041.0 113.0 EWR MSY New Orleans LA
3040738.0 0.0 EWR MSY New Orleans LA
3050727.0 -4.0 EWR MSY New Orleans LA
3052041.0 4.0 EWR MSY New Orleans LA
3060705.0 -1.0 EWR MSY New Orleans LA
3061252.0 67.0 EWR MSY New Orleans LA
3062100.0 21.0 EWR MSY New Orleans LA
3070705.0 -7.0 EWR MSY New Orleans LA
3071252.0 -3.0 EWR MSY New Orleans LA
3072100.0 13.0 EWR MSY New Orleans LA
3080705.0 -5.0 EWR MSY New Orleans LA
3081250.0 5.0 EWR MSY New Orleans LA
3091255.0 -9.0 EWR MSY New Orleans LA
3092059.0 -3.0 EWR MSY New Orleans LA
3100705.0 -5.0 EWR MSY New Orleans LA
3101252.0 -9.0 EWR MSY New Orleans LA
3102059.0 -4.0 EWR MSY New Orleans LA
3112059.0 181.0 EWR MSY New Orleans LA
3111252.0 -7.0 EWR MSY New Orleans LA
3122059.0 22.0 EWR MSY New Orleans LA
3121252.0 161.0 EWR MSY New Orleans LA
3130705.0 0.0 EWR MSY New Orleans LA
3131252.0 0.0 EWR MSY New Orleans LA
3132059.0 0.0 EWR MSY New Orleans LA
3140705.0 66.0 EWR MSY New Orleans LA
3141252.0 39.0 EWR MSY New Orleans LA
3142059.0 -6.0 EWR MSY New Orleans LA
3150700.0 2.0 EWR MSY New Orleans LA
3151250.0 34.0 EWR MSY New Orleans LA
3161255.0 -2.0 EWR MSY New Orleans LA
3162059.0 -1.0 EWR MSY New Orleans LA
3170705.0 -11.0 EWR MSY New Orleans LA
3171252.0 22.0 EWR MSY New Orleans LA
3172059.0 132.0 EWR MSY New Orleans LA
3182059.0 -5.0 EWR MSY New Orleans LA
3181252.0 2.0 EWR MSY New Orleans LA
3192059.0 16.0 EWR MSY New Orleans LA
3191252.0 -4.0 EWR MSY New Orleans LA
3200705.0 -6.0 EWR MSY New Orleans LA
3201252.0 -4.0 EWR MSY New Orleans LA
3202059.0 77.0 EWR MSY New Orleans LA
3210705.0 -4.0 EWR MSY New Orleans LA
3211252.0 9.0 EWR MSY New Orleans LA
3212059.0 11.0 EWR MSY New Orleans LA
3220705.0 -4.0 EWR MSY New Orleans LA
3221250.0 -7.0 EWR MSY New Orleans LA
3221600.0 -6.0 EWR MSY New Orleans LA
3231255.0 5.0 EWR MSY New Orleans LA
3240705.0 -4.0 EWR MSY New Orleans LA
3241252.0 -2.0 EWR MSY New Orleans LA
3242059.0 -10.0 EWR MSY New Orleans LA
3252059.0 121.0 EWR MSY New Orleans LA
3251252.0 -8.0 EWR MSY New Orleans LA
3262059.0 49.0 EWR MSY New Orleans LA
3261252.0 -2.0 EWR MSY New Orleans LA
3270705.0 -6.0 EWR MSY New Orleans LA
3271252.0 -6.0 EWR MSY New Orleans LA
3272059.0 -2.0 EWR MSY New Orleans LA
3280705.0 -3.0 EWR MSY New Orleans LA
3281252.0 -4.0 EWR MSY New Orleans LA
3282059.0 0.0 EWR MSY New Orleans LA
3291250.0 -6.0 EWR MSY New Orleans LA
3290705.0 -1.0 EWR MSY New Orleans LA
3291600.0 1.0 EWR MSY New Orleans LA
3301255.0 64.0 EWR MSY New Orleans LA
3302059.0 166.0 EWR MSY New Orleans LA
3310705.0 -3.0 EWR MSY New Orleans LA
3311252.0 12.0 EWR MSY New Orleans LA
3312059.0 7.0 EWR MSY New Orleans LA
3011902.0 64.0 EWR MSY New Orleans LA
3011536.0 -5.0 EWR MSY New Orleans LA
3021536.0 9.0 EWR MSY New Orleans LA
3031536.0 -10.0 EWR MSY New Orleans LA
3041536.0 -8.0 EWR MSY New Orleans LA
3051536.0 0.0 EWR MSY New Orleans LA
3090715.0 1.0 EWR MSY New Orleans LA
3110705.0 -11.0 EWR MSY New Orleans LA
3120659.0 -5.0 EWR MSY New Orleans LA
3160715.0 -4.0 EWR MSY New Orleans LA
3180705.0 -7.0 EWR MSY New Orleans LA
3190659.0 -2.0 EWR MSY New Orleans LA
3230715.0 -4.0 EWR MSY New Orleans LA
3250705.0 -8.0 EWR MSY New Orleans LA
3260659.0 63.0 EWR MSY New Orleans LA
3300715.0 2.0 EWR MSY New Orleans LA
3010730.0 -4.0 EWR MSY New Orleans LA
3021635.0 16.0 EWR MSY New Orleans LA
3031635.0 -1.0 EWR MSY New Orleans LA
3041635.0 8.0 EWR MSY New Orleans LA
3051635.0 -5.0 EWR MSY New Orleans LA
3061635.0 -2.0 EWR MSY New Orleans LA
3071635.0 5.0 EWR MSY New Orleans LA
3080730.0 -5.0 EWR MSY New Orleans LA
3091825.0 45.0 EWR MSY New Orleans LA
3101825.0 10.0 EWR MSY New Orleans LA
3111825.0 5.0 EWR MSY New Orleans LA
3121825.0 -5.0 EWR MSY New Orleans LA
3131825.0 123.0 EWR MSY New Orleans LA
3141825.0 6.0 EWR MSY New Orleans LA
3150730.0 -3.0 EWR MSY New Orleans LA
3161825.0 24.0 EWR MSY New Orleans LA
3171825.0 6.0 EWR MSY New Orleans LA
3181825.0 -6.0 EWR MSY New Orleans LA
3191825.0 223.0 EWR MSY New Orleans LA
3201825.0 178.0 EWR MSY New Orleans LA
3211825.0 -4.0 EWR MSY New Orleans LA
3220730.0 -3.0 EWR MSY New Orleans LA
3231825.0 -4.0 EWR MSY New Orleans LA
3241825.0 0.0 EWR MSY New Orleans LA
3251825.0 222.0 EWR MSY New Orleans LA
3261825.0 51.0 EWR MSY New Orleans LA
3271825.0 -2.0 EWR MSY New Orleans LA
3281825.0 26.0 EWR MSY New Orleans LA
3290730.0 -3.0 EWR MSY New Orleans LA
3301825.0 139.0 EWR MSY New Orleans LA
3311825.0 25.0 EWR MSY New Orleans LA
1020705.0 0.0 EWR MSY New Orleans LA
1030705.0 0.0 EWR MSY New Orleans LA
1040655.0 0.0 EWR MSY New Orleans LA
1050703.0 4.0 EWR MSY New Orleans LA
1060705.0 36.0 EWR MSY New Orleans LA
1071230.0 24.0 EWR MSY New Orleans LA
1070719.0 -1.0 EWR MSY New Orleans LA
1071730.0 161.0 EWR MSY New Orleans LA
1072043.0 8.0 EWR MSY New Orleans LA
1080719.0 -2.0 EWR MSY New Orleans LA
1081659.0 0.0 EWR MSY New Orleans LA
1081230.0 66.0 EWR MSY New Orleans LA
1082043.0 5.0 EWR MSY New Orleans LA
1090719.0 0.0 EWR MSY New Orleans LA
1091659.0 -1.0 EWR MSY New Orleans LA
1091230.0 -3.0 EWR MSY New Orleans LA
1092043.0 63.0 EWR MSY New Orleans LA
1100719.0 -1.0 EWR MSY New Orleans LA
1101659.0 244.0 EWR MSY New Orleans LA
1101230.0 110.0 EWR MSY New Orleans LA
1102043.0 43.0 EWR MSY New Orleans LA
1111230.0 87.0 EWR MSY New Orleans LA
1110719.0 -1.0 EWR MSY New Orleans LA
1121654.0 -2.0 EWR MSY New Orleans LA
1121230.0 21.0 EWR MSY New Orleans LA
1120709.0 -3.0 EWR MSY New Orleans LA
1122043.0 -1.0 EWR MSY New Orleans LA
1130719.0 -4.0 EWR MSY New Orleans LA
1131659.0 0.0 EWR MSY New Orleans LA
1131230.0 14.0 EWR MSY New Orleans LA
1132043.0 51.0 EWR MSY New Orleans LA
1141230.0 30.0 EWR MSY New Orleans LA
1140719.0 0.0 EWR MSY New Orleans LA
1141730.0 69.0 EWR MSY New Orleans LA
1142043.0 0.0 EWR MSY New Orleans LA
1150719.0 42.0 EWR MSY New Orleans LA
1151659.0 2.0 EWR MSY New Orleans LA
1151230.0 0.0 EWR MSY New Orleans LA
1152043.0 22.0 EWR MSY New Orleans LA
1160719.0 -7.0 EWR MSY New Orleans LA
1161659.0 -9.0 EWR MSY New Orleans LA
1161230.0 -10.0 EWR MSY New Orleans LA
1162043.0 3.0 EWR MSY New Orleans LA
1171659.0 46.0 EWR MSY New Orleans LA
1170719.0 -4.0 EWR MSY New Orleans LA
1172043.0 54.0 EWR MSY New Orleans LA
1181230.0 20.0 EWR MSY New Orleans LA
1180719.0 -5.0 EWR MSY New Orleans LA
1191654.0 -3.0 EWR MSY New Orleans LA
1191230.0 29.0 EWR MSY New Orleans LA
1190709.0 0.0 EWR MSY New Orleans LA
1192043.0 5.0 EWR MSY New Orleans LA
1201659.0 -1.0 EWR MSY New Orleans LA
1200719.0 -1.0 EWR MSY New Orleans LA
1202043.0 12.0 EWR MSY New Orleans LA
1211230.0 102.0 EWR MSY New Orleans LA
1210719.0 -7.0 EWR MSY New Orleans LA
1211730.0 0.0 EWR MSY New Orleans LA
1212043.0 0.0 EWR MSY New Orleans LA
1220719.0 0.0 EWR MSY New Orleans LA
1221659.0 6.0 EWR MSY New Orleans LA
1221230.0 -2.0 EWR MSY New Orleans LA
1222043.0 70.0 EWR MSY New Orleans LA
1230719.0 12.0 EWR MSY New Orleans LA
1231659.0 94.0 EWR MSY New Orleans LA
1231230.0 111.0 EWR MSY New Orleans LA
1232043.0 13.0 EWR MSY New Orleans LA
1240719.0 -1.0 EWR MSY New Orleans LA
1241659.0 84.0 EWR MSY New Orleans LA
1241230.0 0.0 EWR MSY New Orleans LA
1242043.0 56.0 EWR MSY New Orleans LA
1251230.0 -5.0 EWR MSY New Orleans LA
1250719.0 23.0 EWR MSY New Orleans LA
1261654.0 113.0 EWR MSY New Orleans LA
1261230.0 8.0 EWR MSY New Orleans LA
1260709.0 -7.0 EWR MSY New Orleans LA
1262043.0 31.0 EWR MSY New Orleans LA
1271659.0 0.0 EWR MSY New Orleans LA
1270719.0 -3.0 EWR MSY New Orleans LA
1281230.0 0.0 EWR MSY New Orleans LA
1280719.0 0.0 EWR MSY New Orleans LA
1281730.0 0.0 EWR MSY New Orleans LA
1282043.0 0.0 EWR MSY New Orleans LA
1291659.0 -6.0 EWR MSY New Orleans LA
1290719.0 -2.0 EWR MSY New Orleans LA
1292043.0 -8.0 EWR MSY New Orleans LA
1300719.0 2.0 EWR MSY New Orleans LA
1301659.0 9.0 EWR MSY New Orleans LA
1301230.0 0.0 EWR MSY New Orleans LA
1302043.0 93.0 EWR MSY New Orleans LA
1310719.0 34.0 EWR MSY New Orleans LA
1311659.0 0.0 EWR MSY New Orleans LA
1311230.0 5.0 EWR MSY New Orleans LA
1312043.0 28.0 EWR MSY New Orleans LA
1171230.0 -5.0 EWR MSY New Orleans LA
1201250.0 -12.0 EWR MSY New Orleans LA
1291230.0 -2.0 EWR MSY New Orleans LA
1011815.0 125.0 EWR MSY New Orleans LA
1021815.0 33.0 EWR MSY New Orleans LA
1031815.0 0.0 EWR MSY New Orleans LA
1040755.0 0.0 EWR MSY New Orleans LA
1051815.0 172.0 EWR MSY New Orleans LA
1061815.0 151.0 EWR MSY New Orleans LA
1071815.0 43.0 EWR MSY New Orleans LA
1081815.0 14.0 EWR MSY New Orleans LA
1091815.0 3.0 EWR MSY New Orleans LA
1101815.0 10.0 EWR MSY New Orleans LA
1110730.0 0.0 EWR MSY New Orleans LA
1121815.0 0.0 EWR MSY New Orleans LA
1131815.0 -2.0 EWR MSY New Orleans LA
1141815.0 1.0 EWR MSY New Orleans LA
1151815.0 1.0 EWR MSY New Orleans LA
1161815.0 8.0 EWR MSY New Orleans LA
1171815.0 22.0 EWR MSY New Orleans LA
1180730.0 1.0 EWR MSY New Orleans LA
1191815.0 5.0 EWR MSY New Orleans LA
1201815.0 5.0 EWR MSY New Orleans LA
1211815.0 0.0 EWR MSY New Orleans LA
1221815.0 3.0 EWR MSY New Orleans LA
1231815.0 0.0 EWR MSY New Orleans LA
1241815.0 84.0 EWR MSY New Orleans LA
1250730.0 -7.0 EWR MSY New Orleans LA
1261815.0 -5.0 EWR MSY New Orleans LA
1271815.0 -1.0 EWR MSY New Orleans LA
1281815.0 0.0 EWR MSY New Orleans LA
1291815.0 -3.0 EWR MSY New Orleans LA
1301815.0 -5.0 EWR MSY New Orleans LA
1311815.0 -8.0 EWR MSY New Orleans LA
2011055.0 -5.0 LAS MSY New Orleans LA
2021025.0 3.0 LAS MSY New Orleans LA
2031025.0 0.0 LAS MSY New Orleans LA
2031750.0 -2.0 LAS MSY New Orleans LA
2041025.0 6.0 LAS MSY New Orleans LA
2041750.0 -6.0 LAS MSY New Orleans LA
2051025.0 -4.0 LAS MSY New Orleans LA
2051750.0 29.0 LAS MSY New Orleans LA
2061025.0 1.0 LAS MSY New Orleans LA
2061750.0 48.0 LAS MSY New Orleans LA
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1301455.0 6.0 BNA MSY New Orleans LA
1300850.0 -1.0 BNA MSY New Orleans LA
1301735.0 57.0 BNA MSY New Orleans LA
1311240.0 31.0 BNA MSY New Orleans LA
1311455.0 22.0 BNA MSY New Orleans LA
1310850.0 -2.0 BNA MSY New Orleans LA
1311735.0 6.0 BNA MSY New Orleans LA
2011305.0 -1.0 BNA MSY New Orleans LA
2011805.0 -6.0 BNA MSY New Orleans LA
2010950.0 4.0 BNA MSY New Orleans LA
2021455.0 11.0 BNA MSY New Orleans LA
2021235.0 26.0 BNA MSY New Orleans LA
2021650.0 77.0 BNA MSY New Orleans LA
2031240.0 30.0 BNA MSY New Orleans LA
2031455.0 10.0 BNA MSY New Orleans LA
2030850.0 -1.0 BNA MSY New Orleans LA
2031735.0 -3.0 BNA MSY New Orleans LA
2041240.0 56.0 BNA MSY New Orleans LA
2041455.0 23.0 BNA MSY New Orleans LA
2040850.0 -1.0 BNA MSY New Orleans LA
2041735.0 3.0 BNA MSY New Orleans LA
2051240.0 33.0 BNA MSY New Orleans LA
2051455.0 14.0 BNA MSY New Orleans LA
2050850.0 19.0 BNA MSY New Orleans LA
2051735.0 26.0 BNA MSY New Orleans LA
2061240.0 43.0 BNA MSY New Orleans LA
2061455.0 -2.0 BNA MSY New Orleans LA
2060850.0 10.0 BNA MSY New Orleans LA
2061735.0 34.0 BNA MSY New Orleans LA
2071240.0 88.0 BNA MSY New Orleans LA
2071455.0 5.0 BNA MSY New Orleans LA
2070850.0 1.0 BNA MSY New Orleans LA
2071735.0 20.0 BNA MSY New Orleans LA
2081305.0 -6.0 BNA MSY New Orleans LA
2081805.0 0.0 BNA MSY New Orleans LA
2080950.0 -3.0 BNA MSY New Orleans LA
2091235.0 5.0 BNA MSY New Orleans LA
2091455.0 2.0 BNA MSY New Orleans LA
2091735.0 8.0 BNA MSY New Orleans LA
2101240.0 25.0 BNA MSY New Orleans LA
2101455.0 14.0 BNA MSY New Orleans LA
2100850.0 9.0 BNA MSY New Orleans LA
2101735.0 -2.0 BNA MSY New Orleans LA
2111240.0 145.0 BNA MSY New Orleans LA
2111455.0 -2.0 BNA MSY New Orleans LA
2110850.0 96.0 BNA MSY New Orleans LA
2111735.0 25.0 BNA MSY New Orleans LA
2121240.0 8.0 BNA MSY New Orleans LA
2121455.0 29.0 BNA MSY New Orleans LA
2120850.0 3.0 BNA MSY New Orleans LA
2121735.0 39.0 BNA MSY New Orleans LA
2131350.0 5.0 BNA MSY New Orleans LA
2131610.0 11.0 BNA MSY New Orleans LA
2130810.0 -4.0 BNA MSY New Orleans LA
2131800.0 9.0 BNA MSY New Orleans LA
2141610.0 37.0 BNA MSY New Orleans LA
2140810.0 13.0 BNA MSY New Orleans LA
2141350.0 46.0 BNA MSY New Orleans LA
2141800.0 21.0 BNA MSY New Orleans LA
// Build `tripGraph` GraphFrame
// This GraphFrame builds up on the vertices and edges based on our trips (flights)
val tripGraph = GraphFrame(tripVertices, tripEdges)
println(tripGraph)

// Build `tripGraphPrime` GraphFrame
// This graphframe contains a smaller subset of data to make it easier to display motifs and subgraphs (below)
val tripEdgesPrime = departureDelays_geo.select("tripid", "delay", "src", "dst")
val tripGraphPrime = GraphFrame(tripVertices, tripEdgesPrime)
GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripGraph: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripEdgesPrime: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 2 more fields]
tripGraphPrime: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 2 more fields])

Simple Queries

Let's start with a set of simple graph queries to understand flight performance and departure delays

println(s"Airports: ${tripGraph.vertices.count()}")
println(s"Trips: ${tripGraph.edges.count()}")
Airports: 279
Trips: 1361141
// Finding the longest Delay
val longestDelay = tripGraph.edges.groupBy().max("delay")
display(longestDelay)
max(delay)
1642.0
1642.0/60.0
res13: Double = 27.366666666666667
// Determining number of on-time / early flights vs. delayed flights
println(s"On-time / Early Flights: ${tripGraph.edges.filter("delay <= 0").count()}")
println(s"Delayed Flights: ${tripGraph.edges.filter("delay > 0").count()}")
On-time / Early Flights: 780469
Delayed Flights: 580672

What flights departing SFO are most likely to have significant delays

Note, delay can be <= 0 meaning the flight left on time or early

//tripGraph.createOrReplaceTempView("tripgraph")
val sfoDelayedTrips = tripGraph.edges.
  filter("src = 'SFO' and delay > 0").
  groupBy("src", "dst").
  avg("delay").
  sort(desc("avg(delay)"))
sfoDelayedTrips: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
display(sfoDelayedTrips)
src dst avg(delay)
SFO OKC 59.073170731707314
SFO JAC 57.13333333333333
SFO COS 53.976190476190474
SFO OTH 48.09090909090909
SFO SAT 47.625
SFO MOD 46.80952380952381
SFO SUN 46.723404255319146
SFO CIC 46.72164948453608
SFO ABQ 44.8125
SFO ASE 44.285714285714285
SFO PIT 43.875
SFO MIA 43.81730769230769
SFO FAT 43.23972602739726
SFO MFR 43.11848341232228
SFO SBP 43.09770114942529
SFO MSP 42.766917293233085
SFO BOI 42.65482233502538
SFO RDM 41.98823529411764
SFO AUS 41.690677966101696
SFO SLC 41.407272727272726
SFO JFK 41.01379310344828
SFO PSP 40.909909909909906
SFO PHX 40.67272727272727
SFO MRY 40.61764705882353
SFO ACV 40.3728813559322
SFO LAS 40.107602339181284
SFO TUS 39.853658536585364
SFO SAN 38.97361809045226
SFO SBA 38.758620689655174
SFO BFL 38.51136363636363
SFO RDU 38.170731707317074
SFO STL 38.13513513513514
SFO IND 38.114285714285714
SFO EUG 37.573913043478264
SFO RNO 36.81372549019608
SFO BUR 36.75675675675676
SFO LGB 36.752941176470586
SFO HNL 36.25367647058823
SFO LAX 36.165543071161046
SFO RDD 36.11009174311926
SFO MSY 35.421052631578945
SFO SMF 34.936
SFO MDW 34.824742268041234
SFO FLL 34.76842105263158
SFO SEA 34.68854961832061
SFO MCI 34.68571428571428
SFO DFW 34.36642599277978
SFO OGG 34.171875
SFO PDX 34.14430894308943
SFO ORD 33.991130820399114
SFO LIH 32.93023255813954
SFO DEN 32.861491628614914
SFO PSC 32.604651162790695
SFO PHL 32.440677966101696
SFO BWI 31.70212765957447
SFO ONT 31.49079754601227
SFO SNA 31.18426103646833
SFO MCO 31.03488372093023
SFO MKE 31.03448275862069
SFO CLE 30.979591836734695
SFO EWR 30.354285714285716
SFO BOS 29.623471882640587
SFO LMT 29.233333333333334
SFO DTW 28.34722222222222
SFO IAH 28.322105263157894
SFO CVG 27.03125
SFO ATL 26.84860557768924
SFO IAD 26.125964010282775
SFO ANC 25.5
SFO BZN 23.964285714285715
SFO CLT 22.636363636363637
SFO DCA 21.896103896103895
// After displaying tripDelays, use Plot Options to set `state_dst` as a Key.
val tripDelays = tripGraph.edges.filter($"delay" > 0)
display(tripDelays)
tripid delay src dst city_dst state_dst
1021111.0 7.0 MSP INL International Falls MN
1061115.0 33.0 MSP INL International Falls MN
1071115.0 23.0 MSP INL International Falls MN
1091115.0 11.0 MSP INL International Falls MN
1171115.0 4.0 MSP INL International Falls MN
2091925.0 1.0 MSP INL International Falls MN
2152015.0 16.0 MSP INL International Falls MN
2161925.0 169.0 MSP INL International Falls MN
2171115.0 27.0 MSP INL International Falls MN
2181115.0 96.0 MSP INL International Falls MN
2281115.0 5.0 MSP INL International Falls MN
3031115.0 17.0 MSP INL International Falls MN
3171115.0 25.0 MSP INL International Falls MN
3181115.0 2.0 MSP INL International Falls MN
3271115.0 9.0 MSP INL International Falls MN
2020709.0 59.0 EWR MSY New Orleans LA
2021654.0 21.0 EWR MSY New Orleans LA
2041230.0 24.0 EWR MSY New Orleans LA
2040719.0 168.0 EWR MSY New Orleans LA
2041730.0 88.0 EWR MSY New Orleans LA
2042043.0 106.0 EWR MSY New Orleans LA
2052043.0 46.0 EWR MSY New Orleans LA
2061659.0 82.0 EWR MSY New Orleans LA
2061230.0 61.0 EWR MSY New Orleans LA
2062043.0 7.0 EWR MSY New Orleans LA
2070719.0 8.0 EWR MSY New Orleans LA
2071659.0 19.0 EWR MSY New Orleans LA
2071230.0 27.0 EWR MSY New Orleans LA
2072048.0 47.0 EWR MSY New Orleans LA
2091229.0 95.0 EWR MSY New Orleans LA
2092043.0 32.0 EWR MSY New Orleans LA
2100719.0 14.0 EWR MSY New Orleans LA
2101659.0 16.0 EWR MSY New Orleans LA
2111230.0 10.0 EWR MSY New Orleans LA
2110719.0 46.0 EWR MSY New Orleans LA
2120719.0 1.0 EWR MSY New Orleans LA
2120929.0 89.0 EWR MSY New Orleans LA
2122043.0 36.0 EWR MSY New Orleans LA
2182041.0 36.0 EWR MSY New Orleans LA
2190727.0 15.0 EWR MSY New Orleans LA
2202041.0 51.0 EWR MSY New Orleans LA
2220659.0 8.0 EWR MSY New Orleans LA
2232041.0 8.0 EWR MSY New Orleans LA
2240729.0 6.0 EWR MSY New Orleans LA
2260727.0 23.0 EWR MSY New Orleans LA
2262041.0 174.0 EWR MSY New Orleans LA
2270738.0 8.0 EWR MSY New Orleans LA
2272041.0 32.0 EWR MSY New Orleans LA
2280729.0 12.0 EWR MSY New Orleans LA
2282041.0 49.0 EWR MSY New Orleans LA
2281000.0 2.0 EWR MSY New Orleans LA
2051230.0 216.0 EWR MSY New Orleans LA
2131536.0 273.0 EWR MSY New Orleans LA
2141536.0 6.0 EWR MSY New Orleans LA
2151902.0 31.0 EWR MSY New Orleans LA
2151536.0 66.0 EWR MSY New Orleans LA
2161536.0 7.0 EWR MSY New Orleans LA
2171536.0 3.0 EWR MSY New Orleans LA
2181536.0 26.0 EWR MSY New Orleans LA
2211536.0 34.0 EWR MSY New Orleans LA
2221536.0 65.0 EWR MSY New Orleans LA
2281536.0 8.0 EWR MSY New Orleans LA
2111815.0 4.0 EWR MSY New Orleans LA
2121815.0 64.0 EWR MSY New Orleans LA
2141635.0 52.0 EWR MSY New Orleans LA
2161635.0 21.0 EWR MSY New Orleans LA
2171635.0 23.0 EWR MSY New Orleans LA
2191635.0 21.0 EWR MSY New Orleans LA
2211635.0 292.0 EWR MSY New Orleans LA
2220730.0 28.0 EWR MSY New Orleans LA
2251635.0 6.0 EWR MSY New Orleans LA
2261635.0 10.0 EWR MSY New Orleans LA
3032041.0 113.0 EWR MSY New Orleans LA
3052041.0 4.0 EWR MSY New Orleans LA
3061252.0 67.0 EWR MSY New Orleans LA
3062100.0 21.0 EWR MSY New Orleans LA
3072100.0 13.0 EWR MSY New Orleans LA
3081250.0 5.0 EWR MSY New Orleans LA
3112059.0 181.0 EWR MSY New Orleans LA
3122059.0 22.0 EWR MSY New Orleans LA
3121252.0 161.0 EWR MSY New Orleans LA
3140705.0 66.0 EWR MSY New Orleans LA
3141252.0 39.0 EWR MSY New Orleans LA
3150700.0 2.0 EWR MSY New Orleans LA
3151250.0 34.0 EWR MSY New Orleans LA
3171252.0 22.0 EWR MSY New Orleans LA
3172059.0 132.0 EWR MSY New Orleans LA
3181252.0 2.0 EWR MSY New Orleans LA
3192059.0 16.0 EWR MSY New Orleans LA
3202059.0 77.0 EWR MSY New Orleans LA
3211252.0 9.0 EWR MSY New Orleans LA
3212059.0 11.0 EWR MSY New Orleans LA
3231255.0 5.0 EWR MSY New Orleans LA
3252059.0 121.0 EWR MSY New Orleans LA
3262059.0 49.0 EWR MSY New Orleans LA
3291600.0 1.0 EWR MSY New Orleans LA
3301255.0 64.0 EWR MSY New Orleans LA
3302059.0 166.0 EWR MSY New Orleans LA
3311252.0 12.0 EWR MSY New Orleans LA
3312059.0 7.0 EWR MSY New Orleans LA
3011902.0 64.0 EWR MSY New Orleans LA
3021536.0 9.0 EWR MSY New Orleans LA
3090715.0 1.0 EWR MSY New Orleans LA
3260659.0 63.0 EWR MSY New Orleans LA
3300715.0 2.0 EWR MSY New Orleans LA
3021635.0 16.0 EWR MSY New Orleans LA
3041635.0 8.0 EWR MSY New Orleans LA
3071635.0 5.0 EWR MSY New Orleans LA
3091825.0 45.0 EWR MSY New Orleans LA
3101825.0 10.0 EWR MSY New Orleans LA
3111825.0 5.0 EWR MSY New Orleans LA
3131825.0 123.0 EWR MSY New Orleans LA
3141825.0 6.0 EWR MSY New Orleans LA
3161825.0 24.0 EWR MSY New Orleans LA
3171825.0 6.0 EWR MSY New Orleans LA
3191825.0 223.0 EWR MSY New Orleans LA
3201825.0 178.0 EWR MSY New Orleans LA
3251825.0 222.0 EWR MSY New Orleans LA
3261825.0 51.0 EWR MSY New Orleans LA
3281825.0 26.0 EWR MSY New Orleans LA
3301825.0 139.0 EWR MSY New Orleans LA
3311825.0 25.0 EWR MSY New Orleans LA
1050703.0 4.0 EWR MSY New Orleans LA
1060705.0 36.0 EWR MSY New Orleans LA
1071230.0 24.0 EWR MSY New Orleans LA
1071730.0 161.0 EWR MSY New Orleans LA
1072043.0 8.0 EWR MSY New Orleans LA
1081230.0 66.0 EWR MSY New Orleans LA
1082043.0 5.0 EWR MSY New Orleans LA
1092043.0 63.0 EWR MSY New Orleans LA
1101659.0 244.0 EWR MSY New Orleans LA
1101230.0 110.0 EWR MSY New Orleans LA
1102043.0 43.0 EWR MSY New Orleans LA
1111230.0 87.0 EWR MSY New Orleans LA
1121230.0 21.0 EWR MSY New Orleans LA
1131230.0 14.0 EWR MSY New Orleans LA
1132043.0 51.0 EWR MSY New Orleans LA
1141230.0 30.0 EWR MSY New Orleans LA
1141730.0 69.0 EWR MSY New Orleans LA
1150719.0 42.0 EWR MSY New Orleans LA
1151659.0 2.0 EWR MSY New Orleans LA
1152043.0 22.0 EWR MSY New Orleans LA
1162043.0 3.0 EWR MSY New Orleans LA
1171659.0 46.0 EWR MSY New Orleans LA
1172043.0 54.0 EWR MSY New Orleans LA
1181230.0 20.0 EWR MSY New Orleans LA
1191230.0 29.0 EWR MSY New Orleans LA
1192043.0 5.0 EWR MSY New Orleans LA
1202043.0 12.0 EWR MSY New Orleans LA
1211230.0 102.0 EWR MSY New Orleans LA
1221659.0 6.0 EWR MSY New Orleans LA
1222043.0 70.0 EWR MSY New Orleans LA
1230719.0 12.0 EWR MSY New Orleans LA
1231659.0 94.0 EWR MSY New Orleans LA
1231230.0 111.0 EWR MSY New Orleans LA
1232043.0 13.0 EWR MSY New Orleans LA
1241659.0 84.0 EWR MSY New Orleans LA
1242043.0 56.0 EWR MSY New Orleans LA
1250719.0 23.0 EWR MSY New Orleans LA
1261654.0 113.0 EWR MSY New Orleans LA
1261230.0 8.0 EWR MSY New Orleans LA
1262043.0 31.0 EWR MSY New Orleans LA
1300719.0 2.0 EWR MSY New Orleans LA
1301659.0 9.0 EWR MSY New Orleans LA
1302043.0 93.0 EWR MSY New Orleans LA
1310719.0 34.0 EWR MSY New Orleans LA
1311230.0 5.0 EWR MSY New Orleans LA
1312043.0 28.0 EWR MSY New Orleans LA
1011815.0 125.0 EWR MSY New Orleans LA
1021815.0 33.0 EWR MSY New Orleans LA
1051815.0 172.0 EWR MSY New Orleans LA
1061815.0 151.0 EWR MSY New Orleans LA
1071815.0 43.0 EWR MSY New Orleans LA
1081815.0 14.0 EWR MSY New Orleans LA
1091815.0 3.0 EWR MSY New Orleans LA
1101815.0 10.0 EWR MSY New Orleans LA
1141815.0 1.0 EWR MSY New Orleans LA
1151815.0 1.0 EWR MSY New Orleans LA
1161815.0 8.0 EWR MSY New Orleans LA
1171815.0 22.0 EWR MSY New Orleans LA
1180730.0 1.0 EWR MSY New Orleans LA
1191815.0 5.0 EWR MSY New Orleans LA
1201815.0 5.0 EWR MSY New Orleans LA
1221815.0 3.0 EWR MSY New Orleans LA
1241815.0 84.0 EWR MSY New Orleans LA
2021025.0 3.0 LAS MSY New Orleans LA
2041025.0 6.0 LAS MSY New Orleans LA
2051750.0 29.0 LAS MSY New Orleans LA
2061025.0 1.0 LAS MSY New Orleans LA
2061750.0 48.0 LAS MSY New Orleans LA
2071750.0 20.0 LAS MSY New Orleans LA
2081055.0 2.0 LAS MSY New Orleans LA
2091750.0 33.0 LAS MSY New Orleans LA
2111025.0 15.0 LAS MSY New Orleans LA
2121750.0 158.0 LAS MSY New Orleans LA
2131855.0 11.0 LAS MSY New Orleans LA
2131215.0 23.0 LAS MSY New Orleans LA
2141855.0 22.0 LAS MSY New Orleans LA
2141215.0 18.0 LAS MSY New Orleans LA
2151635.0 4.0 LAS MSY New Orleans LA
2161855.0 58.0 LAS MSY New Orleans LA
2161215.0 17.0 LAS MSY New Orleans LA
2171855.0 14.0 LAS MSY New Orleans LA
2171215.0 10.0 LAS MSY New Orleans LA
2181855.0 1.0 LAS MSY New Orleans LA
2181215.0 58.0 LAS MSY New Orleans LA
2191215.0 32.0 LAS MSY New Orleans LA
2201855.0 14.0 LAS MSY New Orleans LA
2201215.0 28.0 LAS MSY New Orleans LA
2211855.0 4.0 LAS MSY New Orleans LA
2211215.0 11.0 LAS MSY New Orleans LA
2221635.0 133.0 LAS MSY New Orleans LA
2231215.0 10.0 LAS MSY New Orleans LA
2241215.0 16.0 LAS MSY New Orleans LA
2251855.0 7.0 LAS MSY New Orleans LA
2251215.0 15.0 LAS MSY New Orleans LA
2261855.0 2.0 LAS MSY New Orleans LA
2261215.0 16.0 LAS MSY New Orleans LA
2271855.0 10.0 LAS MSY New Orleans LA
2271215.0 32.0 LAS MSY New Orleans LA
2281855.0 61.0 LAS MSY New Orleans LA
2281215.0 94.0 LAS MSY New Orleans LA
3010935.0 10.0 LAS MSY New Orleans LA
3011635.0 39.0 LAS MSY New Orleans LA
3031855.0 25.0 LAS MSY New Orleans LA
3031215.0 8.0 LAS MSY New Orleans LA
3041215.0 28.0 LAS MSY New Orleans LA
3051215.0 27.0 LAS MSY New Orleans LA
3061855.0 20.0 LAS MSY New Orleans LA
3061215.0 10.0 LAS MSY New Orleans LA
3071855.0 36.0 LAS MSY New Orleans LA
3071215.0 14.0 LAS MSY New Orleans LA
3080830.0 6.0 LAS MSY New Orleans LA
3091905.0 4.0 LAS MSY New Orleans LA
3101905.0 49.0 LAS MSY New Orleans LA
3111905.0 9.0 LAS MSY New Orleans LA
3110840.0 84.0 LAS MSY New Orleans LA
3121905.0 26.0 LAS MSY New Orleans LA
3131905.0 37.0 LAS MSY New Orleans LA
3130840.0 1.0 LAS MSY New Orleans LA
3141905.0 14.0 LAS MSY New Orleans LA
3150830.0 16.0 LAS MSY New Orleans LA
3151940.0 95.0 LAS MSY New Orleans LA
3171905.0 13.0 LAS MSY New Orleans LA
3170840.0 3.0 LAS MSY New Orleans LA
3181905.0 52.0 LAS MSY New Orleans LA
3180840.0 2.0 LAS MSY New Orleans LA
3201905.0 36.0 LAS MSY New Orleans LA
3200840.0 68.0 LAS MSY New Orleans LA
3220830.0 12.0 LAS MSY New Orleans LA
3221940.0 11.0 LAS MSY New Orleans LA
3231905.0 9.0 LAS MSY New Orleans LA
3230840.0 16.0 LAS MSY New Orleans LA
3241905.0 9.0 LAS MSY New Orleans LA
3240840.0 4.0 LAS MSY New Orleans LA
3251905.0 10.0 LAS MSY New Orleans LA
3261905.0 14.0 LAS MSY New Orleans LA
3260840.0 3.0 LAS MSY New Orleans LA
3271905.0 13.0 LAS MSY New Orleans LA
3280840.0 33.0 LAS MSY New Orleans LA
3291940.0 231.0 LAS MSY New Orleans LA
3301905.0 7.0 LAS MSY New Orleans LA
3300840.0 15.0 LAS MSY New Orleans LA
3311905.0 19.0 LAS MSY New Orleans LA
1011755.0 160.0 LAS MSY New Orleans LA
1021805.0 138.0 LAS MSY New Orleans LA
1020905.0 5.0 LAS MSY New Orleans LA
1031805.0 154.0 LAS MSY New Orleans LA
1030905.0 179.0 LAS MSY New Orleans LA
1041655.0 113.0 LAS MSY New Orleans LA
1040900.0 56.0 LAS MSY New Orleans LA
1051805.0 53.0 LAS MSY New Orleans LA
1061755.0 61.0 LAS MSY New Orleans LA
1060905.0 23.0 LAS MSY New Orleans LA
1071025.0 1.0 LAS MSY New Orleans LA
1071750.0 302.0 LAS MSY New Orleans LA
1081025.0 7.0 LAS MSY New Orleans LA
1081750.0 52.0 LAS MSY New Orleans LA
1091750.0 8.0 LAS MSY New Orleans LA
1101750.0 92.0 LAS MSY New Orleans LA
1111055.0 31.0 LAS MSY New Orleans LA
1121025.0 1.0 LAS MSY New Orleans LA
1141750.0 127.0 LAS MSY New Orleans LA
1151025.0 1.0 LAS MSY New Orleans LA
1171025.0 12.0 LAS MSY New Orleans LA
1191025.0 3.0 LAS MSY New Orleans LA
1201750.0 6.0 LAS MSY New Orleans LA
1211025.0 3.0 LAS MSY New Orleans LA
1211750.0 4.0 LAS MSY New Orleans LA
1221750.0 3.0 LAS MSY New Orleans LA
1231750.0 30.0 LAS MSY New Orleans LA
1241025.0 43.0 LAS MSY New Orleans LA
1251055.0 5.0 LAS MSY New Orleans LA
1261750.0 27.0 LAS MSY New Orleans LA
1271750.0 2.0 LAS MSY New Orleans LA
1291025.0 1.0 LAS MSY New Orleans LA
1301750.0 35.0 LAS MSY New Orleans LA
1311025.0 11.0 LAS MSY New Orleans LA
1311750.0 25.0 LAS MSY New Orleans LA
2041105.0 1.0 MCI MSY New Orleans LA
2051105.0 6.0 MCI MSY New Orleans LA
2061105.0 40.0 MCI MSY New Orleans LA
2071105.0 7.0 MCI MSY New Orleans LA
2081530.0 2.0 MCI MSY New Orleans LA
2121105.0 7.0 MCI MSY New Orleans LA
2140830.0 3.0 MCI MSY New Orleans LA
2150750.0 7.0 MCI MSY New Orleans LA
2170830.0 10.0 MCI MSY New Orleans LA
2180830.0 2.0 MCI MSY New Orleans LA
2200830.0 9.0 MCI MSY New Orleans LA
2230930.0 2.0 MCI MSY New Orleans LA
2240830.0 3.0 MCI MSY New Orleans LA
2260830.0 251.0 MCI MSY New Orleans LA
3020930.0 35.0 MCI MSY New Orleans LA
3030830.0 1.0 MCI MSY New Orleans LA
3040830.0 11.0 MCI MSY New Orleans LA
3050830.0 2.0 MCI MSY New Orleans LA
3081610.0 93.0 MCI MSY New Orleans LA
3100950.0 8.0 MCI MSY New Orleans LA
3110950.0 36.0 MCI MSY New Orleans LA
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3151440.0 5.0 DAL MSY New Orleans LA
3161900.0 15.0 DAL MSY New Orleans LA
3162055.0 22.0 DAL MSY New Orleans LA
3161810.0 5.0 DAL MSY New Orleans LA
3160930.0 7.0 DAL MSY New Orleans LA
3171900.0 83.0 DAL MSY New Orleans LA
3170930.0 1.0 DAL MSY New Orleans LA
3172055.0 81.0 DAL MSY New Orleans LA
3170825.0 7.0 DAL MSY New Orleans LA
3171810.0 20.0 DAL MSY New Orleans LA
3181900.0 46.0 DAL MSY New Orleans LA
3182055.0 148.0 DAL MSY New Orleans LA
3180600.0 8.0 DAL MSY New Orleans LA
3181205.0 32.0 DAL MSY New Orleans LA
3180825.0 7.0 DAL MSY New Orleans LA
3181810.0 92.0 DAL MSY New Orleans LA
3181455.0 11.0 DAL MSY New Orleans LA
3191900.0 29.0 DAL MSY New Orleans LA
3192055.0 71.0 DAL MSY New Orleans LA
3191205.0 10.0 DAL MSY New Orleans LA
3191810.0 11.0 DAL MSY New Orleans LA
3191455.0 2.0 DAL MSY New Orleans LA
3201900.0 26.0 DAL MSY New Orleans LA
3202055.0 34.0 DAL MSY New Orleans LA
3200600.0 19.0 DAL MSY New Orleans LA
3201205.0 4.0 DAL MSY New Orleans LA
3200825.0 5.0 DAL MSY New Orleans LA
3201810.0 31.0 DAL MSY New Orleans LA
3211900.0 52.0 DAL MSY New Orleans LA
3212055.0 23.0 DAL MSY New Orleans LA
3210600.0 1.0 DAL MSY New Orleans LA
3211205.0 29.0 DAL MSY New Orleans LA
3210825.0 13.0 DAL MSY New Orleans LA
3211810.0 14.0 DAL MSY New Orleans LA
3211455.0 9.0 DAL MSY New Orleans LA
3221750.0 7.0 DAL MSY New Orleans LA
3221440.0 23.0 DAL MSY New Orleans LA
3231900.0 36.0 DAL MSY New Orleans LA
3231810.0 6.0 DAL MSY New Orleans LA
3231455.0 8.0 DAL MSY New Orleans LA
3231210.0 16.0 DAL MSY New Orleans LA
3231045.0 13.0 DAL MSY New Orleans LA
3230930.0 6.0 DAL MSY New Orleans LA
3241900.0 15.0 DAL MSY New Orleans LA
3242055.0 36.0 DAL MSY New Orleans LA
3241205.0 23.0 DAL MSY New Orleans LA
3240825.0 5.0 DAL MSY New Orleans LA
3241810.0 9.0 DAL MSY New Orleans LA
3241455.0 17.0 DAL MSY New Orleans LA
3251900.0 58.0 DAL MSY New Orleans LA
3252055.0 51.0 DAL MSY New Orleans LA
3251205.0 5.0 DAL MSY New Orleans LA
3250825.0 5.0 DAL MSY New Orleans LA
3251810.0 17.0 DAL MSY New Orleans LA
3251455.0 10.0 DAL MSY New Orleans LA
3261900.0 15.0 DAL MSY New Orleans LA
3262055.0 69.0 DAL MSY New Orleans LA
3261205.0 6.0 DAL MSY New Orleans LA
3260825.0 3.0 DAL MSY New Orleans LA
3261810.0 20.0 DAL MSY New Orleans LA
3261455.0 10.0 DAL MSY New Orleans LA
3271900.0 36.0 DAL MSY New Orleans LA
3272055.0 78.0 DAL MSY New Orleans LA
3271205.0 17.0 DAL MSY New Orleans LA
3270825.0 8.0 DAL MSY New Orleans LA
3271810.0 45.0 DAL MSY New Orleans LA
3271455.0 11.0 DAL MSY New Orleans LA
3281900.0 46.0 DAL MSY New Orleans LA
3280930.0 11.0 DAL MSY New Orleans LA
3282055.0 65.0 DAL MSY New Orleans LA
3281205.0 13.0 DAL MSY New Orleans LA
3280825.0 6.0 DAL MSY New Orleans LA
3281810.0 49.0 DAL MSY New Orleans LA
3281455.0 1.0 DAL MSY New Orleans LA
3290830.0 1.0 DAL MSY New Orleans LA
3291440.0 7.0 DAL MSY New Orleans LA
3301900.0 126.0 DAL MSY New Orleans LA
3301810.0 4.0 DAL MSY New Orleans LA
3301455.0 4.0 DAL MSY New Orleans LA
3301045.0 29.0 DAL MSY New Orleans LA
3300930.0 4.0 DAL MSY New Orleans LA
3311900.0 47.0 DAL MSY New Orleans LA
3310930.0 10.0 DAL MSY New Orleans LA
3312055.0 3.0 DAL MSY New Orleans LA
3311205.0 20.0 DAL MSY New Orleans LA
3310825.0 21.0 DAL MSY New Orleans LA
3311810.0 18.0 DAL MSY New Orleans LA
3311455.0 3.0 DAL MSY New Orleans LA
1012115.0 24.0 DAL MSY New Orleans LA
1011235.0 8.0 DAL MSY New Orleans LA
1011650.0 70.0 DAL MSY New Orleans LA
1011525.0 21.0 DAL MSY New Orleans LA
1011120.0 5.0 DAL MSY New Orleans LA
1021120.0 40.0 DAL MSY New Orleans LA
1021650.0 37.0 DAL MSY New Orleans LA
1020925.0 3.0 DAL MSY New Orleans LA
1022110.0 115.0 DAL MSY New Orleans LA
1021235.0 66.0 DAL MSY New Orleans LA
1020605.0 31.0 DAL MSY New Orleans LA
1021525.0 91.0 DAL MSY New Orleans LA
1021910.0 12.0 DAL MSY New Orleans LA
1031120.0 121.0 DAL MSY New Orleans LA
1031650.0 154.0 DAL MSY New Orleans LA
1030925.0 46.0 DAL MSY New Orleans LA
1032110.0 120.0 DAL MSY New Orleans LA
1031235.0 43.0 DAL MSY New Orleans LA
1031525.0 45.0 DAL MSY New Orleans LA
1031910.0 194.0 DAL MSY New Orleans LA
1040710.0 6.0 DAL MSY New Orleans LA
1041730.0 108.0 DAL MSY New Orleans LA
1041420.0 49.0 DAL MSY New Orleans LA
1040930.0 45.0 DAL MSY New Orleans LA
1041540.0 1.0 DAL MSY New Orleans LA
1052110.0 110.0 DAL MSY New Orleans LA
1051235.0 3.0 DAL MSY New Orleans LA
1051525.0 24.0 DAL MSY New Orleans LA
1051150.0 32.0 DAL MSY New Orleans LA
1051910.0 29.0 DAL MSY New Orleans LA
1050925.0 11.0 DAL MSY New Orleans LA
1051650.0 101.0 DAL MSY New Orleans LA
1061120.0 48.0 DAL MSY New Orleans LA
1061650.0 29.0 DAL MSY New Orleans LA
1062110.0 103.0 DAL MSY New Orleans LA
1061235.0 71.0 DAL MSY New Orleans LA
1061525.0 18.0 DAL MSY New Orleans LA
1072015.0 46.0 DAL MSY New Orleans LA
1071535.0 53.0 DAL MSY New Orleans LA
1070630.0 1.0 DAL MSY New Orleans LA
1071930.0 1.0 DAL MSY New Orleans LA
1070925.0 17.0 DAL MSY New Orleans LA
1071025.0 22.0 DAL MSY New Orleans LA
1071230.0 2.0 DAL MSY New Orleans LA
1071635.0 2.0 DAL MSY New Orleans LA
1082015.0 28.0 DAL MSY New Orleans LA
1081535.0 31.0 DAL MSY New Orleans LA
1081930.0 16.0 DAL MSY New Orleans LA
1080925.0 4.0 DAL MSY New Orleans LA
1081025.0 24.0 DAL MSY New Orleans LA
1081230.0 8.0 DAL MSY New Orleans LA
1081635.0 29.0 DAL MSY New Orleans LA
1092015.0 89.0 DAL MSY New Orleans LA
1091535.0 20.0 DAL MSY New Orleans LA
1090925.0 39.0 DAL MSY New Orleans LA
1091025.0 94.0 DAL MSY New Orleans LA
1091230.0 73.0 DAL MSY New Orleans LA
1091635.0 35.0 DAL MSY New Orleans LA
1102015.0 100.0 DAL MSY New Orleans LA
1101535.0 70.0 DAL MSY New Orleans LA
1101930.0 118.0 DAL MSY New Orleans LA
1100925.0 10.0 DAL MSY New Orleans LA
1101025.0 9.0 DAL MSY New Orleans LA
1101230.0 21.0 DAL MSY New Orleans LA
1101635.0 57.0 DAL MSY New Orleans LA
1111750.0 36.0 DAL MSY New Orleans LA
1110645.0 2.0 DAL MSY New Orleans LA
1111450.0 3.0 DAL MSY New Orleans LA
1111625.0 50.0 DAL MSY New Orleans LA
1122015.0 2.0 DAL MSY New Orleans LA
1121535.0 8.0 DAL MSY New Orleans LA
1121620.0 33.0 DAL MSY New Orleans LA
1121930.0 23.0 DAL MSY New Orleans LA
1120925.0 3.0 DAL MSY New Orleans LA
1121230.0 6.0 DAL MSY New Orleans LA
1131535.0 3.0 DAL MSY New Orleans LA
1130630.0 2.0 DAL MSY New Orleans LA
1130925.0 9.0 DAL MSY New Orleans LA
1131025.0 18.0 DAL MSY New Orleans LA
1142015.0 10.0 DAL MSY New Orleans LA
1140630.0 5.0 DAL MSY New Orleans LA
1140925.0 12.0 DAL MSY New Orleans LA
1141025.0 16.0 DAL MSY New Orleans LA
1141230.0 3.0 DAL MSY New Orleans LA
1141635.0 5.0 DAL MSY New Orleans LA
1151930.0 1.0 DAL MSY New Orleans LA
1150925.0 8.0 DAL MSY New Orleans LA
1151025.0 19.0 DAL MSY New Orleans LA
1162015.0 6.0 DAL MSY New Orleans LA
1161535.0 26.0 DAL MSY New Orleans LA
1161930.0 5.0 DAL MSY New Orleans LA
1160925.0 10.0 DAL MSY New Orleans LA
1161025.0 23.0 DAL MSY New Orleans LA
1161230.0 4.0 DAL MSY New Orleans LA
1161635.0 28.0 DAL MSY New Orleans LA
1172015.0 17.0 DAL MSY New Orleans LA
1171535.0 2.0 DAL MSY New Orleans LA
1171025.0 3.0 DAL MSY New Orleans LA
1171230.0 17.0 DAL MSY New Orleans LA
1171635.0 30.0 DAL MSY New Orleans LA
1180645.0 19.0 DAL MSY New Orleans LA
1181450.0 3.0 DAL MSY New Orleans LA
1181625.0 7.0 DAL MSY New Orleans LA
1191535.0 23.0 DAL MSY New Orleans LA
1191620.0 4.0 DAL MSY New Orleans LA
1191930.0 219.0 DAL MSY New Orleans LA
1190925.0 5.0 DAL MSY New Orleans LA
1200925.0 12.0 DAL MSY New Orleans LA
1201025.0 33.0 DAL MSY New Orleans LA
1201230.0 1.0 DAL MSY New Orleans LA
1201635.0 6.0 DAL MSY New Orleans LA
1211535.0 95.0 DAL MSY New Orleans LA
// States with the longest cumulative delays (with individual delays > 100 minutes) (origin: Seattle)
display(tripGraph.edges.filter($"src" === "SEA" && $"delay" > 100))
tripid delay src dst city_dst state_dst
3201938.0 108.0 SEA BUR Burbank CA
3201655.0 107.0 SEA SNA Orange County CA
1011950.0 123.0 SEA OAK Oakland CA
1021950.0 194.0 SEA OAK Oakland CA
1021615.0 317.0 SEA OAK Oakland CA
1021755.0 385.0 SEA OAK Oakland CA
1031950.0 283.0 SEA OAK Oakland CA
1031615.0 364.0 SEA OAK Oakland CA
1031325.0 130.0 SEA OAK Oakland CA
1061755.0 107.0 SEA OAK Oakland CA
1081330.0 118.0 SEA OAK Oakland CA
2282055.0 150.0 SEA OAK Oakland CA
3061600.0 130.0 SEA OAK Oakland CA
3170815.0 199.0 SEA DCA Washington DC null
2151845.0 128.0 SEA KTN Ketchikan AK
2281845.0 104.0 SEA KTN Ketchikan AK
3130720.0 117.0 SEA KTN Ketchikan AK
1011411.0 177.0 SEA IAH Houston TX
1022347.0 158.0 SEA IAH Houston TX
1021411.0 170.0 SEA IAH Houston TX
1031201.0 116.0 SEA IAH Houston TX
1031900.0 178.0 SEA IAH Houston TX
1042347.0 114.0 SEA IAH Houston TX
1062347.0 136.0 SEA IAH Houston TX
1101203.0 173.0 SEA IAH Houston TX
1172300.0 125.0 SEA IAH Houston TX
1250605.0 227.0 SEA IAH Houston TX
1291203.0 106.0 SEA IAH Houston TX
2141157.0 127.0 SEA IAH Houston TX
2150740.0 466.0 SEA IAH Houston TX
2180750.0 105.0 SEA IAH Houston TX
3010740.0 103.0 SEA IAH Houston TX
3021152.0 124.0 SEA IAH Houston TX
3121152.0 340.0 SEA IAH Houston TX
3140600.0 139.0 SEA IAH Houston TX
3171152.0 121.0 SEA IAH Houston TX
3280600.0 103.0 SEA IAH Houston TX
1151746.0 229.0 SEA HNL Honolulu, Oahu HI
1271746.0 111.0 SEA HNL Honolulu, Oahu HI
1030840.0 294.0 SEA HNL Honolulu, Oahu HI
2091035.0 132.0 SEA HNL Honolulu, Oahu HI
3141035.0 295.0 SEA HNL Honolulu, Oahu HI
3141930.0 128.0 SEA HNL Honolulu, Oahu HI
3311930.0 104.0 SEA HNL Honolulu, Oahu HI
3030840.0 185.0 SEA HNL Honolulu, Oahu HI
3240845.0 114.0 SEA HNL Honolulu, Oahu HI
1041740.0 256.0 SEA SJC San Jose CA
1061815.0 200.0 SEA SJC San Jose CA
3031600.0 161.0 SEA SJC San Jose CA
1031100.0 145.0 SEA LGB Long Beach CA
1041737.0 320.0 SEA LGB Long Beach CA
1081728.0 122.0 SEA LGB Long Beach CA
1221140.0 189.0 SEA LGB Long Beach CA
2121140.0 365.0 SEA LGB Long Beach CA
3070710.0 115.0 SEA LGB Long Beach CA
3180935.0 135.0 SEA LGB Long Beach CA
2141245.0 176.0 SEA RNO Reno NV
1052147.0 110.0 SEA BOS Boston MA
1081420.0 109.0 SEA BOS Boston MA
1112313.0 110.0 SEA BOS Boston MA
1232313.0 110.0 SEA BOS Boston MA
2140945.0 105.0 SEA BOS Boston MA
2032313.0 114.0 SEA BOS Boston MA
2121420.0 108.0 SEA BOS Boston MA
2141415.0 152.0 SEA BOS Boston MA
3282307.0 119.0 SEA BOS Boston MA
1110905.0 130.0 SEA EWR Newark NJ
1022227.0 236.0 SEA EWR Newark NJ
1180703.0 138.0 SEA EWR Newark NJ
2030905.0 212.0 SEA EWR Newark NJ
3141530.0 131.0 SEA EWR Newark NJ
3171530.0 181.0 SEA EWR Newark NJ
3202200.0 168.0 SEA EWR Newark NJ
1032115.0 196.0 SEA LAS Las Vegas NV
1201840.0 113.0 SEA LAS Las Vegas NV
1260840.0 165.0 SEA LAS Las Vegas NV
1261840.0 121.0 SEA LAS Las Vegas NV
1031905.0 103.0 SEA LAS Las Vegas NV
1041550.0 156.0 SEA LAS Las Vegas NV
1061820.0 213.0 SEA LAS Las Vegas NV
1061515.0 116.0 SEA LAS Las Vegas NV
1080805.0 235.0 SEA LAS Las Vegas NV
1170800.0 247.0 SEA LAS Las Vegas NV
2191235.0 210.0 SEA LAS Las Vegas NV
2281430.0 121.0 SEA LAS Las Vegas NV
2281235.0 187.0 SEA LAS Las Vegas NV
2170830.0 610.0 SEA LAS Las Vegas NV
2051205.0 110.0 SEA LAS Las Vegas NV
2111835.0 133.0 SEA LAS Las Vegas NV
2132005.0 135.0 SEA LAS Las Vegas NV
2272005.0 102.0 SEA LAS Las Vegas NV
3031430.0 108.0 SEA LAS Las Vegas NV
3141410.0 102.0 SEA LAS Las Vegas NV
3151205.0 103.0 SEA LAS Las Vegas NV
3281245.0 140.0 SEA LAS Las Vegas NV
3181520.0 103.0 SEA LAS Las Vegas NV
3292000.0 107.0 SEA LAS Las Vegas NV
1051955.0 160.0 SEA FAI Fairbanks AK
1040955.0 126.0 SEA DEN Denver CO
1040650.0 103.0 SEA DEN Denver CO
1160650.0 120.0 SEA DEN Denver CO
1011940.0 167.0 SEA DEN Denver CO
1041039.0 113.0 SEA DEN Denver CO
1041437.0 256.0 SEA DEN Denver CO
1041937.0 191.0 SEA DEN Denver CO
1040605.0 138.0 SEA DEN Denver CO
1061937.0 111.0 SEA DEN Denver CO
1121937.0 118.0 SEA DEN Denver CO
1171937.0 104.0 SEA DEN Denver CO
1021523.0 118.0 SEA DEN Denver CO
1041521.0 177.0 SEA DEN Denver CO
1061055.0 154.0 SEA DEN Denver CO
1281515.0 137.0 SEA DEN Denver CO
1011450.0 102.0 SEA DEN Denver CO
1021205.0 425.0 SEA DEN Denver CO
1031450.0 211.0 SEA DEN Denver CO
2210955.0 132.0 SEA DEN Denver CO
2111537.0 156.0 SEA DEN Denver CO
2110700.0 625.0 SEA DEN Denver CO
2191039.0 148.0 SEA DEN Denver CO
2211039.0 105.0 SEA DEN Denver CO
2031055.0 174.0 SEA DEN Denver CO
2051055.0 112.0 SEA DEN Denver CO
2211110.0 106.0 SEA DEN Denver CO
3131405.0 154.0 SEA DEN Denver CO
3181930.0 115.0 SEA DEN Denver CO
3191749.0 103.0 SEA DEN Denver CO
3011405.0 109.0 SEA DEN Denver CO
3121519.0 231.0 SEA DEN Denver CO
3061445.0 148.0 SEA DEN Denver CO
3181440.0 132.0 SEA DEN Denver CO
1011306.0 206.0 SEA IAD Washington DC null
1050806.0 105.0 SEA IAD Washington DC null
1052226.0 111.0 SEA IAD Washington DC null
1291256.0 108.0 SEA IAD Washington DC null
2031256.0 185.0 SEA IAD Washington DC null
2041256.0 129.0 SEA IAD Washington DC null
2101256.0 144.0 SEA IAD Washington DC null
2191316.0 122.0 SEA IAD Washington DC null
3080800.0 273.0 SEA IAD Washington DC null
3162225.0 174.0 SEA IAD Washington DC null
3201316.0 111.0 SEA IAD Washington DC null
3261311.0 104.0 SEA IAD Washington DC null
1111300.0 133.0 SEA PSP Palm Springs CA
1110910.0 171.0 SEA PSP Palm Springs CA
3170915.0 114.0 SEA PSP Palm Springs CA
3310645.0 179.0 SEA PSP Palm Springs CA
1161050.0 132.0 SEA SBA Santa Barbara CA
2091050.0 310.0 SEA SBA Santa Barbara CA
3191045.0 109.0 SEA SBA Santa Barbara CA
3241045.0 140.0 SEA SBA Santa Barbara CA
2122225.0 228.0 SEA CLT Charlotte NC
2142225.0 104.0 SEA CLT Charlotte NC
2152225.0 113.0 SEA CLT Charlotte NC
3081110.0 110.0 SEA CLT Charlotte NC
1290940.0 316.0 SEA ABQ Albuquerque NM
1171153.0 119.0 SEA PDX Portland OR
1171439.0 112.0 SEA PDX Portland OR
2091041.0 309.0 SEA PDX Portland OR
2090845.0 133.0 SEA PDX Portland OR
3021455.0 226.0 SEA PDX Portland OR
3021914.0 165.0 SEA PDX Portland OR
3090926.0 112.0 SEA PDX Portland OR
3251041.0 274.0 SEA PDX Portland OR
3261728.0 109.0 SEA PDX Portland OR
3301728.0 108.0 SEA PDX Portland OR
1112205.0 101.0 SEA MIA Miami FL
1262205.0 130.0 SEA MIA Miami FL
2142215.0 229.0 SEA MIA Miami FL
3292220.0 117.0 SEA MIA Miami FL
1012140.0 136.0 SEA SMF Sacramento CA
1010715.0 164.0 SEA SMF Sacramento CA
1021930.0 109.0 SEA SMF Sacramento CA
1031630.0 113.0 SEA SMF Sacramento CA
1041440.0 137.0 SEA SMF Sacramento CA
1041710.0 102.0 SEA SMF Sacramento CA
2211915.0 103.0 SEA SMF Sacramento CA
3262140.0 113.0 SEA SMF Sacramento CA
3271855.0 113.0 SEA SMF Sacramento CA
3191020.0 139.0 SEA SMF Sacramento CA
1291410.0 136.0 SEA PHX Phoenix AZ
1040830.0 294.0 SEA PHX Phoenix AZ
1300830.0 371.0 SEA PHX Phoenix AZ
1031510.0 119.0 SEA PHX Phoenix AZ
1061510.0 180.0 SEA PHX Phoenix AZ
1290720.0 147.0 SEA PHX Phoenix AZ
2082020.0 130.0 SEA PHX Phoenix AZ
2281855.0 186.0 SEA PHX Phoenix AZ
2110520.0 463.0 SEA PHX Phoenix AZ
2111132.0 107.0 SEA PHX Phoenix AZ
2160830.0 110.0 SEA PHX Phoenix AZ
2241132.0 107.0 SEA PHX Phoenix AZ
2251615.0 103.0 SEA PHX Phoenix AZ
3041455.0 108.0 SEA PHX Phoenix AZ
3051745.0 169.0 SEA PHX Phoenix AZ
3091455.0 126.0 SEA PHX Phoenix AZ
3251132.0 123.0 SEA PHX Phoenix AZ
3221530.0 121.0 SEA PHX Phoenix AZ
1060830.0 116.0 SEA DFW Dallas TX
1180830.0 132.0 SEA DFW Dallas TX
1281350.0 115.0 SEA DFW Dallas TX
1291545.0 247.0 SEA DFW Dallas TX
2031545.0 230.0 SEA DFW Dallas TX
2040830.0 135.0 SEA DFW Dallas TX
2061115.0 110.0 SEA DFW Dallas TX
2061545.0 125.0 SEA DFW Dallas TX
2061350.0 126.0 SEA DFW Dallas TX
2080830.0 356.0 SEA DFW Dallas TX
2090830.0 128.0 SEA DFW Dallas TX
3012320.0 123.0 SEA DFW Dallas TX
3152320.0 143.0 SEA DFW Dallas TX
3151400.0 146.0 SEA DFW Dallas TX
3301400.0 277.0 SEA DFW Dallas TX
1171220.0 110.0 SEA SFO San Francisco CA
1291220.0 103.0 SEA SFO San Francisco CA
1031125.0 107.0 SEA SFO San Francisco CA
1120924.0 268.0 SEA SFO San Francisco CA
1161058.0 131.0 SEA SFO San Francisco CA
1171058.0 138.0 SEA SFO San Francisco CA
1010955.0 104.0 SEA SFO San Francisco CA
1021914.0 105.0 SEA SFO San Francisco CA
1041830.0 131.0 SEA SFO San Francisco CA
1051214.0 139.0 SEA SFO San Francisco CA
1061214.0 384.0 SEA SFO San Francisco CA
1111310.0 124.0 SEA SFO San Francisco CA
1241310.0 133.0 SEA SFO San Francisco CA
1281310.0 250.0 SEA SFO San Francisco CA
1051855.0 166.0 SEA SFO San Francisco CA
2060955.0 148.0 SEA SFO San Francisco CA
2072125.0 105.0 SEA SFO San Francisco CA
2071845.0 203.0 SEA SFO San Francisco CA
2071220.0 139.0 SEA SFO San Francisco CA
2071100.0 113.0 SEA SFO San Francisco CA
2071405.0 144.0 SEA SFO San Francisco CA
2080955.0 121.0 SEA SFO San Francisco CA
2091835.0 124.0 SEA SFO San Francisco CA
2091405.0 101.0 SEA SFO San Francisco CA
2101220.0 134.0 SEA SFO San Francisco CA
2100955.0 107.0 SEA SFO San Francisco CA
2101100.0 105.0 SEA SFO San Francisco CA
2101405.0 105.0 SEA SFO San Francisco CA
2130955.0 108.0 SEA SFO San Francisco CA
2131100.0 139.0 SEA SFO San Francisco CA
2141100.0 107.0 SEA SFO San Francisco CA
2281100.0 130.0 SEA SFO San Francisco CA
2231058.0 136.0 SEA SFO San Francisco CA
2021310.0 115.0 SEA SFO San Francisco CA
2091820.0 149.0 SEA SFO San Francisco CA
2181526.0 106.0 SEA SFO San Francisco CA
2271905.0 154.0 SEA SFO San Francisco CA
2021450.0 139.0 SEA SFO San Francisco CA
2060950.0 150.0 SEA SFO San Francisco CA
2061450.0 140.0 SEA SFO San Francisco CA
2061855.0 119.0 SEA SFO San Francisco CA
2071855.0 146.0 SEA SFO San Francisco CA
2081330.0 164.0 SEA SFO San Francisco CA
2091450.0 106.0 SEA SFO San Francisco CA
2091855.0 182.0 SEA SFO San Francisco CA
2261450.0 259.0 SEA SFO San Francisco CA
2261855.0 224.0 SEA SFO San Francisco CA
2270950.0 174.0 SEA SFO San Francisco CA
2271450.0 240.0 SEA SFO San Francisco CA
2280950.0 207.0 SEA SFO San Francisco CA
2281450.0 331.0 SEA SFO San Francisco CA
2281855.0 124.0 SEA SFO San Francisco CA
3142043.0 101.0 SEA SFO San Francisco CA
3180950.0 155.0 SEA SFO San Francisco CA
3260950.0 117.0 SEA SFO San Francisco CA
3281430.0 112.0 SEA SFO San Francisco CA
3291050.0 138.0 SEA SFO San Francisco CA
3062055.0 113.0 SEA SFO San Francisco CA
3091028.0 143.0 SEA SFO San Francisco CA
3112055.0 116.0 SEA SFO San Francisco CA
3261043.0 121.0 SEA SFO San Francisco CA
3311043.0 257.0 SEA SFO San Francisco CA
3171237.0 182.0 SEA SFO San Francisco CA
3251933.0 197.0 SEA SFO San Francisco CA
3011330.0 115.0 SEA SFO San Francisco CA
3311045.0 189.0 SEA SFO San Francisco CA
3311440.0 101.0 SEA SFO San Francisco CA
1031152.0 397.0 SEA ATL Atlanta GA
1050830.0 106.0 SEA ATL Atlanta GA
1051152.0 107.0 SEA ATL Atlanta GA
1121159.0 201.0 SEA ATL Atlanta GA
1250630.0 206.0 SEA ATL Atlanta GA
1301159.0 117.0 SEA ATL Atlanta GA
1301322.0 179.0 SEA ATL Atlanta GA
2021159.0 145.0 SEA ATL Atlanta GA
2171320.0 133.0 SEA ATL Atlanta GA
3171320.0 109.0 SEA ATL Atlanta GA
1092015.0 119.0 SEA FAT Fresno CA
2012015.0 189.0 SEA FAT Fresno CA
2091150.0 232.0 SEA FAT Fresno CA
1021425.0 298.0 SEA ORD Chicago IL
1030600.0 103.0 SEA ORD Chicago IL
1081205.0 135.0 SEA ORD Chicago IL
1161205.0 582.0 SEA ORD Chicago IL
1241205.0 174.0 SEA ORD Chicago IL
1300815.0 209.0 SEA ORD Chicago IL
1021235.0 113.0 SEA ORD Chicago IL
1020830.0 151.0 SEA ORD Chicago IL
1051235.0 240.0 SEA ORD Chicago IL
1050830.0 163.0 SEA ORD Chicago IL
1241235.0 171.0 SEA ORD Chicago IL
1301235.0 111.0 SEA ORD Chicago IL
1300830.0 141.0 SEA ORD Chicago IL
1012359.0 227.0 SEA ORD Chicago IL
1040859.0 302.0 SEA ORD Chicago IL
1241110.0 223.0 SEA ORD Chicago IL
1311411.0 142.0 SEA ORD Chicago IL
2051235.0 115.0 SEA ORD Chicago IL
2050830.0 128.0 SEA ORD Chicago IL
2171235.0 204.0 SEA ORD Chicago IL
2170830.0 220.0 SEA ORD Chicago IL
2031615.0 185.0 SEA ORD Chicago IL
2171415.0 164.0 SEA ORD Chicago IL
2261415.0 138.0 SEA ORD Chicago IL
3120820.0 179.0 SEA ORD Chicago IL
3121200.0 151.0 SEA ORD Chicago IL
3200600.0 140.0 SEA ORD Chicago IL
3051235.0 140.0 SEA ORD Chicago IL
3120830.0 204.0 SEA ORD Chicago IL
3132240.0 127.0 SEA ORD Chicago IL
3162240.0 150.0 SEA ORD Chicago IL
3181417.0 127.0 SEA ORD Chicago IL
1091350.0 133.0 SEA MDW Chicago IL
1261350.0 109.0 SEA MDW Chicago IL
3171420.0 111.0 SEA MDW Chicago IL
3310600.0 206.0 SEA MDW Chicago IL
2271820.0 203.0 SEA COS Colorado Springs CO
3171825.0 205.0 SEA COS Colorado Springs CO
1250755.0 233.0 SEA JNU Juneau AK
1310755.0 210.0 SEA JNU Juneau AK
1311120.0 105.0 SEA JNU Juneau AK
3030755.0 110.0 SEA JNU Juneau AK
3130750.0 320.0 SEA JNU Juneau AK
1062320.0 107.0 SEA DTW Detroit MI
3121405.0 135.0 SEA ONT Ontario CA
3130725.0 174.0 SEA ONT Ontario CA
1171425.0 105.0 SEA LAX Los Angeles CA
1041700.0 264.0 SEA LAX Los Angeles CA
1051700.0 136.0 SEA LAX Los Angeles CA
1071645.0 151.0 SEA LAX Los Angeles CA
1311645.0 136.0 SEA LAX Los Angeles CA
1251020.0 130.0 SEA LAX Los Angeles CA
1261020.0 108.0 SEA LAX Los Angeles CA
1291258.0 126.0 SEA LAX Los Angeles CA
2022140.0 131.0 SEA LAX Los Angeles CA
2130610.0 135.0 SEA LAX Los Angeles CA
2091645.0 104.0 SEA LAX Los Angeles CA
2131645.0 134.0 SEA LAX Los Angeles CA
2031805.0 212.0 SEA LAX Los Angeles CA
2071805.0 105.0 SEA LAX Los Angeles CA
2071649.0 126.0 SEA LAX Los Angeles CA
2081649.0 154.0 SEA LAX Los Angeles CA
2091649.0 245.0 SEA LAX Los Angeles CA
2131705.0 107.0 SEA LAX Los Angeles CA
2161010.0 124.0 SEA LAX Los Angeles CA
2271149.0 192.0 SEA LAX Los Angeles CA
3141650.0 197.0 SEA LAX Los Angeles CA
3152105.0 115.0 SEA LAX Los Angeles CA
3171700.0 131.0 SEA LAX Los Angeles CA
3291700.0 118.0 SEA LAX Los Angeles CA
1050037.0 102.0 SEA MSP Minneapolis MN
1070700.0 193.0 SEA MSP Minneapolis MN
1130700.0 429.0 SEA MSP Minneapolis MN
2240655.0 109.0 SEA MSP Minneapolis MN
2121515.0 118.0 SEA MSP Minneapolis MN
1060800.0 132.0 SEA MCO Orlando FL
2220955.0 157.0 SEA SAN San Diego CA
3010955.0 108.0 SEA SAN San Diego CA
1031300.0 108.0 SEA ANC Anchorage AK
1062110.0 149.0 SEA ANC Anchorage AK
1132112.0 106.0 SEA ANC Anchorage AK
2072350.0 141.0 SEA ANC Anchorage AK
2091815.0 133.0 SEA ANC Anchorage AK
3181605.0 187.0 SEA ANC Anchorage AK
3231915.0 115.0 SEA ANC Anchorage AK
3311605.0 135.0 SEA ANC Anchorage AK
1022258.0 180.0 SEA JFK New York NY
1042258.0 285.0 SEA JFK New York NY
1022259.0 116.0 SEA JFK New York NY
2090715.0 110.0 SEA JFK New York NY
2022145.0 101.0 SEA JFK New York NY
2032145.0 165.0 SEA JFK New York NY
2031535.0 181.0 SEA JFK New York NY
2042145.0 201.0 SEA JFK New York NY
2142140.0 113.0 SEA JFK New York NY
2222140.0 118.0 SEA JFK New York NY
2032258.0 130.0 SEA JFK New York NY
2220700.0 404.0 SEA JFK New York NY
3310715.0 385.0 SEA JFK New York NY
3192140.0 119.0 SEA JFK New York NY
3091300.0 125.0 SEA JFK New York NY
1241000.0 806.0 SEA OGG Kahului, Maui HI
1030845.0 342.0 SEA PHL Philadelphia PA
1050845.0 174.0 SEA PHL Philadelphia PA
1210845.0 866.0 SEA PHL Philadelphia PA
1022215.0 121.0 SEA PHL Philadelphia PA
1031130.0 146.0 SEA PHL Philadelphia PA
1090835.0 148.0 SEA PHL Philadelphia PA
1170835.0 203.0 SEA PHL Philadelphia PA
2030845.0 202.0 SEA PHL Philadelphia PA
3130835.0 290.0 SEA PHL Philadelphia PA
1031810.0 142.0 SEA SLC Salt Lake City UT
1041016.0 117.0 SEA SLC Salt Lake City UT
1291725.0 111.0 SEA SLC Salt Lake City UT
1021555.0 175.0 SEA SLC Salt Lake City UT
1041825.0 110.0 SEA SLC Salt Lake City UT
2131635.0 134.0 SEA SLC Salt Lake City UT
2230710.0 739.0 SEA SLC Salt Lake City UT
2240710.0 477.0 SEA SLC Salt Lake City UT
2061005.0 119.0 SEA SLC Salt Lake City UT
3051315.0 123.0 SEA SLC Salt Lake City UT
3260710.0 149.0 SEA SLC Salt Lake City UT
3281750.0 125.0 SEA SLC Salt Lake City UT

Vertex Degrees

  • inDegrees: Incoming connections to the airport
  • outDegrees: Outgoing connections from the airport
  • degrees: Total connections to and from the airport

Reviewing the various properties of the property graph to understand the incoming and outgoing connections between airports.

// Degrees
// The number of degrees - the number of incoming and outgoing connections - for various airports within this sample dataset
display(tripGraph.degrees.sort($"degree".desc).limit(20))
id degree
ATL 179774.0
DFW 133966.0
ORD 125405.0
LAX 106853.0
DEN 103699.0
IAH 85685.0
PHX 79672.0
SFO 77635.0
LAS 66101.0
CLT 56103.0
EWR 54407.0
MCO 54300.0
LGA 50927.0
SLC 50780.0
BOS 49936.0
DTW 46705.0
MSP 46235.0
SEA 45816.0
JFK 43661.0
BWI 42526.0

City / Flight Relationships through Motif Finding

To more easily understand the complex relationship of city airports and their flights with each other, we can use motifs to find patterns of airports (i.e. vertices) connected by flights (i.e. edges). The result is a DataFrame in which the column names are given by the motif keys.

/*
Using tripGraphPrime to more easily display 
- The associated edge (ab, bc) relationships 
- With the different the city / airports (a, b, c) where SFO is the connecting city (b)
- Ensuring that flight ab (i.e. the flight to SFO) occured before flight bc (i.e. flight leaving SFO)
- Note, TripID was generated based on time in the format of MMDDHHMM converted to int
- Therefore bc.tripid < ab.tripid + 10000 means the second flight (bc) occured within approx a day of the first flight (ab)
Note: In reality, we would need to be more careful to link trips ab and bc.
*/
val motifs = tripGraphPrime.
  find("(a)-[ab]->(b); (b)-[bc]->(c)").
  filter("(b.id = 'SFO') and (ab.delay > 500 or bc.delay > 500) and bc.tripid > ab.tripid and bc.tripid < ab.tripid + 10000")

display(motifs)

Determining Airport Ranking using PageRank

There are a large number of flights and connections through these various airports included in this Departure Delay Dataset. Using the pageRank algorithm, Spark iteratively traverses the graph and determines a rough estimate of how important the airport is.

// Determining Airport ranking of importance using `pageRank`
val ranks = tripGraph.pageRank.resetProbability(0.15).maxIter(5).run()
ranks: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 3 more fields], e:[src: string, dst: string ... 5 more fields])
display(ranks.vertices.orderBy($"pagerank".desc).limit(20))
id City State Country pagerank
ATL Atlanta GA USA 18.910104616729814
DFW Dallas TX USA 13.699227467378964
ORD Chicago IL USA 13.163049993795985
DEN Denver CO USA 9.723388283811563
LAX Los Angeles CA USA 8.703656827807166
IAH Houston TX USA 7.991324463091128
SFO San Francisco CA USA 6.903242998287933
PHX Phoenix AZ USA 6.505886984498643
SLC Salt Lake City UT USA 5.799587684561128
LAS Las Vegas NV USA 5.25359244560915
SEA Seattle WA USA 4.626877547905697
EWR Newark NJ USA 4.401221169028188
MCO Orlando FL USA 4.389045874474043
CLT Charlotte NC USA 4.378459524081744
DTW Detroit MI USA 4.223377976049847
MSP Minneapolis MN USA 4.1490048912541795
LGA New York NY USA 4.129454491295321
BOS Boston MA USA 3.812077076528526
BWI Baltimore MD USA 3.53116352570383
JFK New York NY USA 3.521942669296845

BTW, A lot more delicate air-traffic arithmetic is possible for a full month of airplane co-trajectories over the radar range of Atlanta, Georgia, one of the busiest airports in the world.

See for instance:

Using the tripGraph, we can quickly determine what are the most popular single city hop flights

// Determine the most popular flights (single city hops)
import org.apache.spark.sql.functions._

val topTrips = tripGraph.edges.
  groupBy("src", "dst").
  agg(count("delay").as("trips"))
import org.apache.spark.sql.functions._
topTrips: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
// Show the top 20 most popular flights (single city hops)
display(topTrips.orderBy($"trips".desc).limit(20))
src dst trips
SFO LAX 3232.0
LAX SFO 3198.0
LAS LAX 3016.0
LAX LAS 2964.0
JFK LAX 2720.0
LAX JFK 2719.0
ATL LGA 2501.0
LGA ATL 2500.0
LAX PHX 2394.0
PHX LAX 2387.0
HNL OGG 2380.0
OGG HNL 2379.0
LAX SAN 2215.0
SAN LAX 2214.0
SJC LAX 2208.0
LAX SJC 2201.0
ATL MCO 2136.0
MCO ATL 2090.0
JFK SFO 2084.0
SFO JFK 2084.0

Top Transfer Cities

Many airports are used as transfer points instead of the final Destination. An easy way to calculate this is by calculating the ratio of inDegree (the number of flights to the airport) / outDegree (the number of flights leaving the airport). Values close to 1 may indicate many transfers, whereas values < 1 indicate many outgoing flights and > 1 indicate many incoming flights. Note, this is a simple calculation that does not take into account of timing or scheduling of flights, just the overall aggregate number within the dataset.

// Calculate the inDeg (flights into the airport) and outDeg (flights leaving the airport)
val inDeg = tripGraph.inDegrees
val outDeg = tripGraph.outDegrees

// Calculate the degreeRatio (inDeg/outDeg), perform inner join on "id" column
val degreeRatio = inDeg.join(outDeg, inDeg("id") === outDeg("id")).
  drop(outDeg("id")).
  selectExpr("id", "double(inDegree)/double(outDegree) as degreeRatio").
  cache()

// Join back to the `airports` DataFrame (instead of registering temp table as above)
val nonTransferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA").
  selectExpr("id", "city", "degreeRatio").
  filter("degreeRatio < 0.9 or degreeRatio > 1.1")

// List out the city airports which have abnormal degree ratios
display(nonTransferAirports)
id city degreeRatio
GFK Grand Forks 1.3333333333333333
FAI Fairbanks 1.1232686980609419
OME Nome 0.5084745762711864
BRW Barrow 0.28651685393258425
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val transferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA"). //degreeRatio.join(airports, degreeRatio("id") === airports("IATA")).
  selectExpr("id", "city", "degreeRatio").
  filter("degreeRatio between 0.9 and 1.1")
  
// List out the top 10 transfer city airports
display(transferAirports.orderBy("degreeRatio").limit(10))
id city degreeRatio
MSP Minneapolis 0.9375183338222353
DEN Denver 0.958025717037065
DFW Dallas 0.964339653074092
ORD Chicago 0.9671063983310065
SLC Salt Lake City 0.9827417906368358
IAH Houston 0.9846895050147083
PHX Phoenix 0.9891643572266746
OGG Kahului, Maui 0.9898718478710211
HNL Honolulu, Oahu 0.990535889872173
SFO San Francisco 0.9909473252295224

Breadth-first search (BFS) is designed to traverse the graph to quickly find the desired vertices (i.e. airports) and edges (i.e flights). Let's try to find the shortest number of connections between cities based on the dataset. Note, these examples do not take into account of time or distance, just hops between cities.

// Example 1: Direct Seattle to San Francisco
// This method returns a DataFrame of valid shortest paths from vertices matching "fromExpr" to vertices matching "toExpr"
val filteredPaths = tripGraph.bfs.fromExpr((col("id") === "SEA")).toExpr(col("id") === "SFO").maxPathLength(1).run()
display(filteredPaths)

As you can see, there are a number of direct flights between Seattle and San Francisco.

// Example 2: Direct San Francisco and Buffalo
// You can also specify expression as a String, instead of Column
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(1).run()
filteredPaths: org.apache.spark.sql.DataFrame = [id: string, City: string ... 2 more fields]
filteredPaths.show()
+---+----+-----+-------+
| id|City|State|Country|
+---+----+-----+-------+
+---+----+-----+-------+
display(filteredPaths) // display instead of show - same diference

But there are no direct flights between San Francisco and Buffalo.

// Example 2a: Flying from San Francisco to Buffalo
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(2).run()
display(filteredPaths)

But there are flights from San Francisco to Buffalo with Minneapolis as the transfer point.

Loading the D3 Visualization

Using the airports D3 visualization to visualize airports and flight paths

Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3a1.graphs.help()

Produces a force-directed graph given a collection of edges of the following form:
case class Edge(src: String, dest: String, count: Long)

Usage:
%scala
import d3._
graphs.force(
  height = 500,
  width = 500,
  clicks: Dataset[Edge])

// On-time and Early Arrivals
import d3a1._

graphs.force(
  height = 800,
  width = 1200,
  clicks = sql("select src, dst as dest, count(1) as count from departureDelays_geo where delay <= 0 group by src, dst").as[Edge])

You try!

NOW or Later as HOMEWORK

  1. Try to do the same process for the State of the Union Addresses dataset from Week1. As a first step, first locate where that data is... Go to week1 and try to see if each SoU can be treated as a document for topic modeling and whether there is temporal clustering of SoU's within the same topic.

  2. Try to improve the tuning by elaborating the pipeline with stemming, lemmatization, etc in this news-group dataset (if you want to do a project based on this, perhaps). You can also parse the input to bring in the newsgroup id's from the directories (consider exploiting the file names in the wholeTextFiles method) as this will let you explore how well your unsupervised algorithm is doing relative to the known newsgroups each document falls in (note you generally won't have the luxury of knowing the topic labels for typical datasets in the unsupervised topic modeling domain).

  3. Try to parse the data closer to the clean dataset available in /databricks-datasets/news20.binary/* and walk through the following notebook (but in Scala!):

ls /databricks-datasets/news20.binary/data-001
path name size
dbfs:/databricks-datasets/news20.binary/data-001/test/ test/ 0.0
dbfs:/databricks-datasets/news20.binary/data-001/training/ training/ 0.0

Step 1. Downloading and Loading Data into DBFS

you don't have to do the download in databricks if above cell has contents in /databricks-datasets/news20.binary/data-001

Here are the steps taken for downloading and saving data to the distributed file system. Uncomment them for repeating this process on your databricks cluster or for downloading a new source of data.

wget http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz -O /tmp/newsgroups.tar.gz
--2020-11-18 16:58:10--  http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz
Resolving kdd.ics.uci.edu (kdd.ics.uci.edu)... 128.195.1.86
Connecting to kdd.ics.uci.edu (kdd.ics.uci.edu)|128.195.1.86|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1860687 (1.8M) [application/x-gzip]
Saving to: ‘/tmp/newsgroups.tar.gz’

     0K .......... .......... .......... .......... ..........  2%  765K 2s
    50K .......... .......... .......... .......... ..........  5% 1.68M 2s
   100K .......... .......... .......... .......... ..........  8%  165M 1s
   150K .......... .......... .......... .......... .......... 11%  325M 1s
   200K .......... .......... .......... .......... .......... 13% 1.79M 1s
   250K .......... .......... .......... .......... .......... 16%  103M 1s
   300K .......... .......... .......... .......... .......... 19%  116M 1s
   350K .......... .......... .......... .......... .......... 22%  103M 0s
   400K .......... .......... .......... .......... .......... 24% 1.88M 0s
   450K .......... .......... .......... .......... .......... 27%  103M 0s
   500K .......... .......... .......... .......... .......... 30% 90.5M 0s
   550K .......... .......... .......... .......... .......... 33%  133M 0s
   600K .......... .......... .......... .......... .......... 35% 96.6M 0s
   650K .......... .......... .......... .......... .......... 38% 93.3M 0s
   700K .......... .......... .......... .......... .......... 41% 93.7M 0s
   750K .......... .......... .......... .......... .......... 44% 99.0M 0s
   800K .......... .......... .......... .......... .......... 46% 2.07M 0s
   850K .......... .......... .......... .......... .......... 49%  135M 0s
   900K .......... .......... .......... .......... .......... 52%  104M 0s
   950K .......... .......... .......... .......... .......... 55% 96.3M 0s
  1000K .......... .......... .......... .......... .......... 57%  128M 0s
  1050K .......... .......... .......... .......... .......... 60% 91.8M 0s
  1100K .......... .......... .......... .......... .......... 63% 35.4M 0s
  1150K .......... .......... .......... .......... .......... 66% 22.0M 0s
  1200K .......... .......... .......... .......... .......... 68% 28.4M 0s
  1250K .......... .......... .......... .......... .......... 71% 97.4M 0s
  1300K .......... .......... .......... .......... .......... 74%  226M 0s
  1350K .......... .......... .......... .......... .......... 77%  314M 0s
  1400K .......... .......... .......... .......... .......... 79%  363M 0s
  1450K .......... .......... .......... .......... .......... 82%  375M 0s
  1500K .......... .......... .......... .......... .......... 85%  281M 0s
  1550K .......... .......... .......... .......... .......... 88%  364M 0s
  1600K .......... .......... .......... .......... .......... 90% 2.65M 0s
  1650K .......... .......... .......... .......... .......... 93% 34.2M 0s
  1700K .......... .......... .......... .......... .......... 96%  240M 0s
  1750K .......... .......... .......... .......... .......... 99%  310M 0s
  1800K .......... .......                                    100%  211M=0.2s

2020-11-18 16:58:11 (8.63 MB/s) - ‘/tmp/newsgroups.tar.gz’ saved [1860687/1860687]

Untar the file into the /tmp/ folder.

tar xvfz /tmp/newsgroups.tar.gz -C /tmp/
mini_newsgroups/alt.atheism/
mini_newsgroups/alt.atheism/51127
mini_newsgroups/alt.atheism/51310
mini_newsgroups/alt.atheism/53539
mini_newsgroups/alt.atheism/53336
mini_newsgroups/alt.atheism/53212
mini_newsgroups/alt.atheism/51199
mini_newsgroups/alt.atheism/54144
mini_newsgroups/alt.atheism/54170
mini_newsgroups/alt.atheism/51126
mini_newsgroups/alt.atheism/51313
mini_newsgroups/alt.atheism/51166
mini_newsgroups/alt.atheism/53760
mini_newsgroups/alt.atheism/53211
mini_newsgroups/alt.atheism/54251
mini_newsgroups/alt.atheism/53188
mini_newsgroups/alt.atheism/54237
mini_newsgroups/alt.atheism/51227
mini_newsgroups/alt.atheism/51146
mini_newsgroups/alt.atheism/53542
mini_newsgroups/alt.atheism/53291
mini_newsgroups/alt.atheism/53150
mini_newsgroups/alt.atheism/53427
mini_newsgroups/alt.atheism/53061
mini_newsgroups/alt.atheism/53564
mini_newsgroups/alt.atheism/53574
mini_newsgroups/alt.atheism/53351
mini_newsgroups/alt.atheism/53334
mini_newsgroups/alt.atheism/53610
mini_newsgroups/alt.atheism/51195
mini_newsgroups/alt.atheism/53753
mini_newsgroups/alt.atheism/53410
mini_newsgroups/alt.atheism/53303
mini_newsgroups/alt.atheism/53565
mini_newsgroups/alt.atheism/51170
mini_newsgroups/alt.atheism/51305
mini_newsgroups/alt.atheism/54137
mini_newsgroups/alt.atheism/53312
mini_newsgroups/alt.atheism/53575
mini_newsgroups/alt.atheism/53458
mini_newsgroups/alt.atheism/53249
mini_newsgroups/alt.atheism/53299
mini_newsgroups/alt.atheism/53393
mini_newsgroups/alt.atheism/54485
mini_newsgroups/alt.atheism/54254
mini_newsgroups/alt.atheism/54171
mini_newsgroups/alt.atheism/51281
mini_newsgroups/alt.atheism/53607
mini_newsgroups/alt.atheism/53606
mini_newsgroups/alt.atheism/53190
mini_newsgroups/alt.atheism/51223
mini_newsgroups/alt.atheism/51251
mini_newsgroups/alt.atheism/53525
mini_newsgroups/alt.atheism/53154
mini_newsgroups/alt.atheism/53126
mini_newsgroups/alt.atheism/53670
mini_newsgroups/alt.atheism/54250
mini_newsgroups/alt.atheism/53590
mini_newsgroups/alt.atheism/53512
mini_newsgroups/alt.atheism/53518
mini_newsgroups/alt.atheism/53284
mini_newsgroups/alt.atheism/54244
mini_newsgroups/alt.atheism/54215
mini_newsgroups/alt.atheism/54234
mini_newsgroups/alt.atheism/51121
mini_newsgroups/alt.atheism/53222
mini_newsgroups/alt.atheism/53433
mini_newsgroups/alt.atheism/53538
mini_newsgroups/alt.atheism/51203
mini_newsgroups/alt.atheism/53399
mini_newsgroups/alt.atheism/54222
mini_newsgroups/alt.atheism/51314
mini_newsgroups/alt.atheism/53358
mini_newsgroups/alt.atheism/53408
mini_newsgroups/alt.atheism/53599
mini_newsgroups/alt.atheism/51139
mini_newsgroups/alt.atheism/53369
mini_newsgroups/alt.atheism/53474
mini_newsgroups/alt.atheism/53623
mini_newsgroups/alt.atheism/51186
mini_newsgroups/alt.atheism/53653
mini_newsgroups/alt.atheism/53490
mini_newsgroups/alt.atheism/51191
mini_newsgroups/alt.atheism/53235
mini_newsgroups/alt.atheism/53633
mini_newsgroups/alt.atheism/54160
mini_newsgroups/alt.atheism/53420
mini_newsgroups/alt.atheism/51174
mini_newsgroups/alt.atheism/53558
mini_newsgroups/alt.atheism/51222
mini_newsgroups/alt.atheism/53123
mini_newsgroups/alt.atheism/54140
mini_newsgroups/alt.atheism/53659
mini_newsgroups/alt.atheism/53759
mini_newsgroups/alt.atheism/53603
mini_newsgroups/alt.atheism/53459
mini_newsgroups/alt.atheism/53062
mini_newsgroups/alt.atheism/51143
mini_newsgroups/alt.atheism/51131
mini_newsgroups/alt.atheism/54201
mini_newsgroups/alt.atheism/53509
mini_newsgroups/comp.graphics/
mini_newsgroups/comp.graphics/38464
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mini_newsgroups/talk.politics.misc/178851
mini_newsgroups/talk.politics.misc/178799
mini_newsgroups/talk.politics.misc/178924
mini_newsgroups/talk.politics.misc/178721
mini_newsgroups/talk.politics.misc/178451
mini_newsgroups/talk.politics.misc/178792
mini_newsgroups/talk.politics.misc/176881
mini_newsgroups/talk.politics.misc/178654
mini_newsgroups/talk.politics.misc/176951
mini_newsgroups/talk.politics.misc/178801
mini_newsgroups/talk.politics.misc/179018
mini_newsgroups/talk.politics.misc/176886
mini_newsgroups/talk.politics.misc/178360
mini_newsgroups/talk.politics.misc/178699
mini_newsgroups/talk.politics.misc/176988
mini_newsgroups/talk.politics.misc/179097
mini_newsgroups/talk.politics.misc/179067
mini_newsgroups/talk.politics.misc/178318
mini_newsgroups/talk.politics.misc/178447
mini_newsgroups/talk.politics.misc/178455
mini_newsgroups/talk.politics.misc/177008
mini_newsgroups/talk.politics.misc/178527
mini_newsgroups/talk.politics.misc/178337
mini_newsgroups/talk.politics.misc/178556
mini_newsgroups/talk.politics.misc/178522
mini_newsgroups/talk.politics.misc/178517
mini_newsgroups/talk.politics.misc/178793
mini_newsgroups/talk.politics.misc/178579
mini_newsgroups/talk.politics.misc/178837
mini_newsgroups/talk.politics.misc/178731
mini_newsgroups/talk.politics.misc/178668
mini_newsgroups/talk.politics.misc/178939
mini_newsgroups/talk.politics.misc/178560
mini_newsgroups/talk.politics.misc/178571
mini_newsgroups/talk.politics.misc/178487
mini_newsgroups/talk.politics.misc/178965
mini_newsgroups/talk.politics.misc/178998
mini_newsgroups/talk.politics.misc/176982
mini_newsgroups/talk.politics.misc/178606
mini_newsgroups/talk.politics.misc/178341
mini_newsgroups/talk.politics.misc/178622
mini_newsgroups/talk.politics.misc/178309
mini_newsgroups/talk.politics.misc/178769
mini_newsgroups/talk.politics.misc/178927
mini_newsgroups/talk.politics.misc/178751
mini_newsgroups/talk.politics.misc/178349
mini_newsgroups/talk.politics.misc/179095
mini_newsgroups/talk.politics.misc/178993
mini_newsgroups/talk.politics.misc/178361
mini_newsgroups/talk.politics.misc/178610
mini_newsgroups/talk.religion.misc/
mini_newsgroups/talk.religion.misc/83682
mini_newsgroups/talk.religion.misc/83790
mini_newsgroups/talk.religion.misc/83751
mini_newsgroups/talk.religion.misc/83618
mini_newsgroups/talk.religion.misc/83535
mini_newsgroups/talk.religion.misc/84068
mini_newsgroups/talk.religion.misc/83732
mini_newsgroups/talk.religion.misc/83798
mini_newsgroups/talk.religion.misc/83564
mini_newsgroups/talk.religion.misc/84351
mini_newsgroups/talk.religion.misc/84212
mini_newsgroups/talk.religion.misc/83601
mini_newsgroups/talk.religion.misc/83775
mini_newsgroups/talk.religion.misc/83919
mini_newsgroups/talk.religion.misc/84355
mini_newsgroups/talk.religion.misc/82799
mini_newsgroups/talk.religion.misc/83518
mini_newsgroups/talk.religion.misc/84053
mini_newsgroups/talk.religion.misc/83866
mini_newsgroups/talk.religion.misc/83599
mini_newsgroups/talk.religion.misc/83788
mini_newsgroups/talk.religion.misc/84350
mini_newsgroups/talk.religion.misc/83641
mini_newsgroups/talk.religion.misc/84316
mini_newsgroups/talk.religion.misc/83771
mini_newsgroups/talk.religion.misc/83717
mini_newsgroups/talk.religion.misc/83645
mini_newsgroups/talk.religion.misc/83742
mini_newsgroups/talk.religion.misc/83979
mini_newsgroups/talk.religion.misc/84356
mini_newsgroups/talk.religion.misc/83567
mini_newsgroups/talk.religion.misc/84060
mini_newsgroups/talk.religion.misc/83843
mini_newsgroups/talk.religion.misc/84413
mini_newsgroups/talk.religion.misc/83474
mini_newsgroups/talk.religion.misc/84510
mini_newsgroups/talk.religion.misc/83748
mini_newsgroups/talk.religion.misc/83829
mini_newsgroups/talk.religion.misc/83548
mini_newsgroups/talk.religion.misc/84400
mini_newsgroups/talk.religion.misc/83827
mini_newsgroups/talk.religion.misc/84302
mini_newsgroups/talk.religion.misc/82796
mini_newsgroups/talk.religion.misc/84178
mini_newsgroups/talk.religion.misc/84398
mini_newsgroups/talk.religion.misc/84567
mini_newsgroups/talk.religion.misc/84324
mini_newsgroups/talk.religion.misc/82812
mini_newsgroups/talk.religion.misc/83817
mini_newsgroups/talk.religion.misc/83558
mini_newsgroups/talk.religion.misc/83683
mini_newsgroups/talk.religion.misc/84103
mini_newsgroups/talk.religion.misc/83791
mini_newsgroups/talk.religion.misc/84115
mini_newsgroups/talk.religion.misc/83797
mini_newsgroups/talk.religion.misc/83673
mini_newsgroups/talk.religion.misc/84255
mini_newsgroups/talk.religion.misc/84164
mini_newsgroups/talk.religion.misc/83977
mini_newsgroups/talk.religion.misc/84359
mini_newsgroups/talk.religion.misc/83773
mini_newsgroups/talk.religion.misc/84244
mini_newsgroups/talk.religion.misc/84194
mini_newsgroups/talk.religion.misc/84290
mini_newsgroups/talk.religion.misc/84317
mini_newsgroups/talk.religion.misc/84251
mini_newsgroups/talk.religion.misc/83898
mini_newsgroups/talk.religion.misc/83983
mini_newsgroups/talk.religion.misc/83807
mini_newsgroups/talk.religion.misc/84152
mini_newsgroups/talk.religion.misc/83480
mini_newsgroups/talk.religion.misc/82804
mini_newsgroups/talk.religion.misc/82758
mini_newsgroups/talk.religion.misc/83785
mini_newsgroups/talk.religion.misc/84309
mini_newsgroups/talk.religion.misc/84195
mini_newsgroups/talk.religion.misc/83678
mini_newsgroups/talk.religion.misc/84293
mini_newsgroups/talk.religion.misc/84345
mini_newsgroups/talk.religion.misc/83900
mini_newsgroups/talk.religion.misc/84120
mini_newsgroups/talk.religion.misc/83730
mini_newsgroups/talk.religion.misc/83575
mini_newsgroups/talk.religion.misc/83719
mini_newsgroups/talk.religion.misc/83574
mini_newsgroups/talk.religion.misc/84436
mini_newsgroups/talk.religion.misc/83861
mini_newsgroups/talk.religion.misc/83563
mini_newsgroups/talk.religion.misc/84278
mini_newsgroups/talk.religion.misc/83780
mini_newsgroups/talk.religion.misc/83862
mini_newsgroups/talk.religion.misc/83435
mini_newsgroups/talk.religion.misc/83763
mini_newsgroups/talk.religion.misc/83796
mini_newsgroups/talk.religion.misc/84083
mini_newsgroups/talk.religion.misc/83437
mini_newsgroups/talk.religion.misc/83750
mini_newsgroups/talk.religion.misc/83571
mini_newsgroups/talk.religion.misc/83981
mini_newsgroups/talk.religion.misc/82764

The below cell takes about 10mins to run.

NOTE: It is slow partly because each file is small and we are facing the 'small files problem' with distributed file systems that need meta-data for each file. If the file name is not needed then it may be better to create one large stream of the contents of all the files into dbfs. We leave this as it is to show what happens when we upload a dataset of lots of little files into dbfs.

cp -r file:/tmp/mini_newsgroups dbfs:/datasets/mini_newsgroups
res1: Boolean = true
display(dbutils.fs.ls("dbfs:/datasets/mini_newsgroups"))
path name size
dbfs:/datasets/mini_newsgroups/alt.atheism/ alt.atheism/ 0.0
dbfs:/datasets/mini_newsgroups/comp.graphics/ comp.graphics/ 0.0
dbfs:/datasets/mini_newsgroups/comp.os.ms-windows.misc/ comp.os.ms-windows.misc/ 0.0
dbfs:/datasets/mini_newsgroups/comp.sys.ibm.pc.hardware/ comp.sys.ibm.pc.hardware/ 0.0
dbfs:/datasets/mini_newsgroups/comp.sys.mac.hardware/ comp.sys.mac.hardware/ 0.0
dbfs:/datasets/mini_newsgroups/comp.windows.x/ comp.windows.x/ 0.0
dbfs:/datasets/mini_newsgroups/misc.forsale/ misc.forsale/ 0.0
dbfs:/datasets/mini_newsgroups/rec.autos/ rec.autos/ 0.0
dbfs:/datasets/mini_newsgroups/rec.motorcycles/ rec.motorcycles/ 0.0
dbfs:/datasets/mini_newsgroups/rec.sport.baseball/ rec.sport.baseball/ 0.0
dbfs:/datasets/mini_newsgroups/rec.sport.hockey/ rec.sport.hockey/ 0.0
dbfs:/datasets/mini_newsgroups/sci.crypt/ sci.crypt/ 0.0
dbfs:/datasets/mini_newsgroups/sci.electronics/ sci.electronics/ 0.0
dbfs:/datasets/mini_newsgroups/sci.med/ sci.med/ 0.0
dbfs:/datasets/mini_newsgroups/sci.space/ sci.space/ 0.0
dbfs:/datasets/mini_newsgroups/soc.religion.christian/ soc.religion.christian/ 0.0
dbfs:/datasets/mini_newsgroups/talk.politics.guns/ talk.politics.guns/ 0.0
dbfs:/datasets/mini_newsgroups/talk.politics.mideast/ talk.politics.mideast/ 0.0
dbfs:/datasets/mini_newsgroups/talk.politics.misc/ talk.politics.misc/ 0.0
dbfs:/datasets/mini_newsgroups/talk.religion.misc/ talk.religion.misc/ 0.0

ScaDaMaLe Course site and book

Topic Modeling of Movie Dialogs with Latent Dirichlet Allocation

Let us cluster the conversations from different movies!

This notebook will provide a brief algorithm summary, links for further reading, and an example of how to use LDA for Topic Modeling.

not tested in Spark 2.2+ or 3.x yet (see 034 notebook for syntactic issues, if any)

Algorithm Summary

Readings for LDA

Also read the methodological and more formal papers cited in the above links if you want to know more.

Let's get a bird's eye view of LDA from http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf next.

  • See pictures (hopefully you read the paper last night!)
  • Algorithm of the generative model (this is unsupervised clustering)
  • For a careful introduction to the topic see Section 27.3 and 27.4 (pages 950-970) pf Murphy's Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
  • We will be quite application focussed or applied here!

Probabilistic Topic Modeling Example

This is an outline of our Topic Modeling workflow. Feel free to jump to any subtopic to find out more.

  • Step 0. Dataset Review
  • Step 1. Downloading and Loading Data into DBFS
    • (Step 1. only needs to be done once per shard - see details at the end of the notebook for Step 1.)
  • Step 2. Loading the Data and Data Cleaning
  • Step 3. Text Tokenization
  • Step 4. Remove Stopwords
  • Step 5. Vector of Token Counts
  • Step 6. Create LDA model with Online Variational Bayes
  • Step 7. Review Topics
  • Step 8. Model Tuning - Refilter Stopwords
  • Step 9. Create LDA model with Expectation Maximization
  • Step 10. Visualize Results

Step 0. Dataset Review

In this example, we will use the Cornell Movie Dialogs Corpus.

Here is the README.txt:



Cornell Movie-Dialogs Corpus

Distributed together with:

"Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs" Cristian Danescu-Niculescu-Mizil and Lillian Lee Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011.

(this paper is included in this zip file)

NOTE: If you have results to report on these corpora, please send email to cristian@cs.cornell.edu or llee@cs.cornell.edu so we can add you to our list of people using this data. Thanks!

Contents of this README:

    A) Brief description
    B) Files description
    C) Details on the collection procedure
    D) Contact

A) Brief description:

This corpus contains a metadata-rich collection of fictional conversations extracted from raw movie scripts:

  • 220,579 conversational exchanges between 10,292 pairs of movie characters
  • involves 9,035 characters from 617 movies
  • in total 304,713 utterances
  • movie metadata included: - genres - release year - IMDB rating - number of IMDB votes - IMDB rating
  • character metadata included: - gender (for 3,774 characters) - position on movie credits (3,321 characters)

B) Files description:

In all files the field separator is " +++$+++ "

  • movietitlesmetadata.txt - contains information about each movie title - fields: - movieID, - movie title, - movie year, - IMDB rating, - no. IMDB votes, - genres in the format ['genre1','genre2',...,'genreN']

  • moviecharactersmetadata.txt - contains information about each movie character - fields: - characterID - character name - movieID - movie title - gender ("?" for unlabeled cases) - position in credits ("?" for unlabeled cases)

  • movie_lines.txt - contains the actual text of each utterance - fields: - lineID - characterID (who uttered this phrase) - movieID - character name - text of the utterance

  • movieconversations.txt - the structure of the conversations - fields - characterID of the first character involved in the conversation - characterID of the second character involved in the conversation - movieID of the movie in which the conversation occurred - list of the utterances that make the conversation, in chronological order: ['lineID1','lineID2',...,'lineIDN'] has to be matched with movielines.txt to reconstruct the actual content

  • rawscripturls.txt - the urls from which the raw sources were retrieved

C) Details on the collection procedure:

We started from raw publicly available movie scripts (sources acknowledged in rawscripturls.txt). In order to collect the metadata necessary for this study and to distinguish between two script versions of the same movie, we automatically matched each script with an entry in movie database provided by IMDB (The Internet Movie Database; data interfaces available at http://www.imdb.com/interfaces). Some amount of manual correction was also involved. When more than one movie with the same title was found in IMBD, the match was made with the most popular title (the one that received most IMDB votes)

After discarding all movies that could not be matched or that had less than 5 IMDB votes, we were left with 617 unique titles with metadata including genre, release year, IMDB rating and no. of IMDB votes and cast distribution. We then identified the pairs of characters that interact and separated their conversations automatically using simple data processing heuristics. After discarding all pairs that exchanged less than 5 conversational exchanges there were 10,292 left, exchanging 220,579 conversational exchanges (304,713 utterances). After automatically matching the names of the 9,035 involved characters to the list of cast distribution, we used the gender of each interpreting actor to infer the fictional gender of a subset of 3,321 movie characters (we raised the number of gendered 3,774 characters through manual annotation). Similarly, we collected the end credit position of a subset of 3,321 characters as a proxy for their status.

D) Contact:

Please email any questions to: cristian@cs.cornell.edu (Cristian Danescu-Niculescu-Mizil)



Step 2. Loading the Data and Data Cleaning

We have already used the wget command to download the file, and put it in our distributed file system (this process takes about 1 minute). To repeat these steps or to download data from another source follow the steps at the bottom of this worksheet on Step 1. Downloading and Loading Data into DBFS.

Let's make sure these files are in dbfs now:

// this is where the data resides in dbfs (see below to download it first, if you go to a new shard!)
display(dbutils.fs.ls("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/")) 
path name size
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt README.txt 4181.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt movie_characters_metadata.txt 705695.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt movie_conversations.txt 6760930.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt movie_lines.txt 3.4641919e7
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt movie_titles_metadata.txt 67289.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt raw_script_urls.txt 56177.0
sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt").top(5).foreach(println)
u999 +++$+++ u1006 +++$+++ m65 +++$+++ ['L227588', 'L227589', 'L227590', 'L227591', 'L227592', 'L227593', 'L227594', 'L227595', 'L227596']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228159', 'L228160']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228157', 'L228158']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228130', 'L228131']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228127', 'L228128', 'L228129']
// Load text file, leave out file paths, convert all strings to lowercase
val conversationsRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt").zipWithIndex()
conversationsRaw: org.apache.spark.rdd.RDD[(String, Long)] = ZippedWithIndexRDD[3709] at zipWithIndex at command-753740454082219:2

Review first 5 lines to get a sense for the data format.

conversationsRaw.top(5).foreach(println) // the first five Strings in the RDD
(u999 +++$+++ u1006 +++$+++ m65 +++$+++ ['L227588', 'L227589', 'L227590', 'L227591', 'L227592', 'L227593', 'L227594', 'L227595', 'L227596'],8954)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228159', 'L228160'],8952)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228157', 'L228158'],8951)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228130', 'L228131'],8950)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228127', 'L228128', 'L228129'],8949)
conversationsRaw.count // there are over 83,000 conversations in total
res1: Long = 83097
import scala.util.{Failure, Success}

val regexConversation = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(\[.*\]\s*$)""".r

case class conversationLine(a: String, b: String, c: String, d: String)

val conversationsRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt")
 .zipWithIndex()
  .map(x => 
          {
            val id:Long = x._2
            val line = x._1
            val pLine = regexConversation.findFirstMatchIn(line)
                               .map(m => conversationLine(m.group(1), m.group(3), m.group(5), m.group(7))) 
                                  match {
                                    case Some(l) => Success(l)
                                    case None => Failure(new Exception(s"Non matching input: $line"))
                                  }
              (id,pLine)
           }
  )
import scala.util.{Failure, Success}
regexConversation: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(\[.*\]\s*$)
defined class conversationLine
conversationsRaw: org.apache.spark.rdd.RDD[(Long, Product with Serializable with scala.util.Try[conversationLine])] = MapPartitionsRDD[3713] at map at command-753740454082223:9
conversationsRaw.filter(x => x._2.isSuccess).count()
res2: Long = 83097
conversationsRaw.filter(x => x._2.isFailure).count()
res3: Long = 0

The conversation number and line numbers of each conversation are in one line in conversationsRaw.

conversationsRaw.filter(x => x._2.isSuccess).take(5).foreach(println)
(0,Success(conversationLine(u0,u2,m0,['L194', 'L195', 'L196', 'L197'])))
(1,Success(conversationLine(u0,u2,m0,['L198', 'L199'])))
(2,Success(conversationLine(u0,u2,m0,['L200', 'L201', 'L202', 'L203'])))
(3,Success(conversationLine(u0,u2,m0,['L204', 'L205', 'L206'])))
(4,Success(conversationLine(u0,u2,m0,['L207', 'L208'])))

Let's create conversations that have just the coversation id and line-number with order information.

val conversations 
    = conversationsRaw
      .filter(x => x._2.isSuccess)
      .flatMap { 
        case (id,Success(l))  
                  => { val conv = l.d.replace("[","").replace("]","").replace("'","").replace(" ","")
                       val convLinesIndexed = conv.split(",").zipWithIndex
                       convLinesIndexed.map( cLI => (id, cLI._2, cLI._1))
                      }
       }.toDF("conversationID","intraConversationID","lineID")
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: (_, Failure(_))
      .flatMap {
               ^
conversations: org.apache.spark.sql.DataFrame = [conversationID: bigint, intraConversationID: int ... 1 more field]
conversations.show(15)
+--------------+-------------------+------+
|conversationID|intraConversationID|lineID|
+--------------+-------------------+------+
|             0|                  0|  L194|
|             0|                  1|  L195|
|             0|                  2|  L196|
|             0|                  3|  L197|
|             1|                  0|  L198|
|             1|                  1|  L199|
|             2|                  0|  L200|
|             2|                  1|  L201|
|             2|                  2|  L202|
|             2|                  3|  L203|
|             3|                  0|  L204|
|             3|                  1|  L205|
|             3|                  2|  L206|
|             4|                  0|  L207|
|             4|                  1|  L208|
+--------------+-------------------+------+
only showing top 15 rows
val moviesMetaDataRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
moviesMetaDataRaw.top(5).foreach(println)
m99 +++$+++ indiana jones and the temple of doom +++$+++ 1984 +++$+++ 7.50 +++$+++ 112054 +++$+++ ['action', 'adventure']
m98 +++$+++ indiana jones and the last crusade +++$+++ 1989 +++$+++ 8.30 +++$+++ 174947 +++$+++ ['action', 'adventure', 'thriller', 'action', 'adventure', 'fantasy']
m97 +++$+++ independence day +++$+++ 1996 +++$+++ 6.60 +++$+++ 151698 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
m96 +++$+++ invaders from mars +++$+++ 1953 +++$+++ 6.40 +++$+++ 2115 +++$+++ ['horror', 'sci-fi']
m95 +++$+++ i am legend +++$+++ 2007 +++$+++ 7.10 +++$+++ 156084 +++$+++ ['drama', 'sci-fi', 'thriller']
moviesMetaDataRaw: org.apache.spark.rdd.RDD[String] = dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt MapPartitionsRDD[3722] at textFile at command-753740454082232:1
moviesMetaDataRaw.count() // number of movies
res8: Long = 617
import scala.util.{Failure, Success}

/*  - contains information about each movie title
  - fields:
          - movieID,
          - movie title,
          - movie year,
          - IMDB rating,
          - no. IMDB votes,
          - genres in the format ['genre1','genre2',...,'genreN']
          */
val regexMovieMetaData = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(\[.*\]\s*$)""".r

case class lineInMovieMetaData(movieID: String, movieTitle: String, movieYear: String, IMDBRating: String, NumIMDBVotes: String, genres: String)

val moviesMetaDataRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
  .map(line => 
          {
            val pLine = regexMovieMetaData.findFirstMatchIn(line)
                               .map(m => lineInMovieMetaData(m.group(1), m.group(3), m.group(5), m.group(7), m.group(9), m.group(11))) 
                                  match {
                                    case Some(l) => Success(l)
                                    case None => Failure(new Exception(s"Non matching input: $line"))
                                  }
              pLine
           }
  )
import scala.util.{Failure, Success}
regexMovieMetaData: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(\[.*\]\s*$)
defined class lineInMovieMetaData
moviesMetaDataRaw: org.apache.spark.rdd.RDD[Product with Serializable with scala.util.Try[lineInMovieMetaData]] = MapPartitionsRDD[3725] at map at command-753740454082234:17
moviesMetaDataRaw.count
res9: Long = 617
moviesMetaDataRaw.filter(x => x.isSuccess).count()
res10: Long = 617
moviesMetaDataRaw.filter(x => x.isSuccess).take(10).foreach(println)
Success(lineInMovieMetaData(m0,10 things i hate about you,1999,6.90,62847,['comedy', 'romance']))
Success(lineInMovieMetaData(m1,1492: conquest of paradise,1992,6.20,10421,['adventure', 'biography', 'drama', 'history']))
Success(lineInMovieMetaData(m2,15 minutes,2001,6.10,25854,['action', 'crime', 'drama', 'thriller']))
Success(lineInMovieMetaData(m3,2001: a space odyssey,1968,8.40,163227,['adventure', 'mystery', 'sci-fi']))
Success(lineInMovieMetaData(m4,48 hrs.,1982,6.90,22289,['action', 'comedy', 'crime', 'drama', 'thriller']))
Success(lineInMovieMetaData(m5,the fifth element,1997,7.50,133756,['action', 'adventure', 'romance', 'sci-fi', 'thriller']))
Success(lineInMovieMetaData(m6,8mm,1999,6.30,48212,['crime', 'mystery', 'thriller']))
Success(lineInMovieMetaData(m7,a nightmare on elm street 4: the dream master,1988,5.20,13590,['fantasy', 'horror', 'thriller']))
Success(lineInMovieMetaData(m8,a nightmare on elm street: the dream child,1989,4.70,11092,['fantasy', 'horror', 'thriller']))
Success(lineInMovieMetaData(m9,the atomic submarine,1959,4.90,513,['sci-fi', 'thriller']))
//moviesMetaDataRaw.filter(x => x.isFailure).take(10).foreach(println) // to regex refine for casting
val moviesMetaData 
    = moviesMetaDataRaw
      .filter(x => x.isSuccess)
      .map { case Success(l) => l }
      .toDF().select("movieID","movieTitle","movieYear")
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: Failure(_)
      .map { case Success(l) => l }
           ^
moviesMetaData: org.apache.spark.sql.DataFrame = [movieID: string, movieTitle: string ... 1 more field]
moviesMetaData.show(10,false)
+-------+---------------------------------------------+---------+
|movieID|movieTitle                                   |movieYear|
+-------+---------------------------------------------+---------+
|m0     |10 things i hate about you                   |1999     |
|m1     |1492: conquest of paradise                   |1992     |
|m2     |15 minutes                                   |2001     |
|m3     |2001: a space odyssey                        |1968     |
|m4     |48 hrs.                                      |1982     |
|m5     |the fifth element                            |1997     |
|m6     |8mm                                          |1999     |
|m7     |a nightmare on elm street 4: the dream master|1988     |
|m8     |a nightmare on elm street: the dream child   |1989     |
|m9     |the atomic submarine                         |1959     |
+-------+---------------------------------------------+---------+
only showing top 10 rows
val linesRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
linesRaw: org.apache.spark.rdd.RDD[String] = dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt MapPartitionsRDD[3733] at textFile at command-753740454082242:1
linesRaw.count() // number of lines making up the conversations
res15: Long = 304713

Review first 5 lines to get a sense for the data format.

linesRaw.top(5).foreach(println)
L99999 +++$+++ u4166 +++$+++ m278 +++$+++ DULANEY +++$+++ You didn't know about it before that?
L99998 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ To show you this.  It's a letter from that lawyer, Koehler.  He wrote it to me the day after I saw him.  He's the one who told me I could get the money if Miss Lawson went to jail.
L99997 +++$+++ u4166 +++$+++ m278 +++$+++ DULANEY +++$+++ Why'd you come here?
L99996 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ I'm gonna go to jail.  I know they're gonna make it look like I did it. They gotta put it on someone.
L99995 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ What do you think I've got?  A gun? Maybe I'm gonna kill you too.  Maybe I'll blow your head off right now.

To see 5 random lines in the lines.txt evaluate the following cell.

linesRaw.takeSample(false, 5).foreach(println)
L216035 +++$+++ u5302 +++$+++ m351 +++$+++ RAMBO +++$+++ Colonel.
L597568 +++$+++ u8300 +++$+++ m564 +++$+++ LOMBARD +++$+++ I don�t.
L513032 +++$+++ u7667 +++$+++ m518 +++$+++ LINDA +++$+++ He's no more an Indian than I am though. Anyhow, Doyle's gonna try and tease you and be mean to you to show off to his friends. Just like he does to Frank and me sometimes. You just ignore it. Or stay out here away from 'em if he'll let you. He's an okay guy till he gets drunk but tonight he'll get drunk. I guarantee it.
L35914 +++$+++ u313 +++$+++ m19 +++$+++ JESSE +++$+++ Yesss, but I was thinking, I could come by, and then take Zee out. Some place near. With other folk. Near. Here.  But out.
L426481 +++$+++ u2391 +++$+++ m153 +++$+++ COOLEY +++$+++ - and share one of your graves.
import scala.util.{Failure, Success}

/*  field in line.txt are:
          - lineID
          - characterID (who uttered this phrase)
          - movieID
          - character name
          - text of the utterance
          */
val regexLine = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(.+)\s+(\2)\s*(.*$)""".r

case class lineInMovie(lineID: String, characterID: String, movieID: String, characterName: String, text: String)

val linesRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
  .map(line => 
          {
            val pLine = regexLine.findFirstMatchIn(line)
                               .map(m => lineInMovie(m.group(1), m.group(3), m.group(5), m.group(7), m.group(9))) 
                                  match {
                                    case Some(l) => Success(l)
                                    case None => Failure(new Exception(s"Non matching input: $line"))
                                  }
              pLine
           }
  )
import scala.util.{Failure, Success}
regexLine: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(.+)\s+(\2)\s*(.*$)
defined class lineInMovie
linesRaw: org.apache.spark.rdd.RDD[Product with Serializable with scala.util.Try[lineInMovie]] = MapPartitionsRDD[3737] at map at command-753740454082248:15
linesRaw.filter(x => x.isSuccess).count()
res18: Long = 304713
linesRaw.filter(x => x.isFailure).count()
res19: Long = 0
linesRaw.filter(x => x.isSuccess).take(5).foreach(println)
Success(lineInMovie(L1045,u0,m0,BIANCA,They do not!))
Success(lineInMovie(L1044,u2,m0,CAMERON,They do to!))
Success(lineInMovie(L985,u0,m0,BIANCA,I hope so.))
Success(lineInMovie(L984,u2,m0,CAMERON,She okay?))
Success(lineInMovie(L925,u0,m0,BIANCA,Let's go.))

Let's make a DataFrame out of the successfully parsed line.

val lines 
    = linesRaw
      .filter(x => x.isSuccess)
      .map { case Success(l) => l }
      .toDF()
      .join(moviesMetaData, "movieID") // and join it to get movie meta data
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: Failure(_)
      .map { case Success(l) => l }
           ^
lines: org.apache.spark.sql.DataFrame = [movieID: string, lineID: string ... 5 more fields]
lines.show(5)
+-------+-------+-----------+-------------+--------------------+-------------+---------+
|movieID| lineID|characterID|characterName|                text|   movieTitle|movieYear|
+-------+-------+-----------+-------------+--------------------+-------------+---------+
|   m203|L593445|      u3102|        HAGEN|You owe the Don a...|the godfather|     1972|
|   m203|L593444|      u3094|     BONASERA|Yes, I understand...|the godfather|     1972|
|   m203|L593443|      u3102|        HAGEN|This is Tom Hagen...|the godfather|     1972|
|   m203|L593425|      u3102|        HAGEN|               Yes. |the godfather|     1972|
|   m203|L593424|      u3094|     BONASERA|The Don himself i...|the godfather|     1972|
+-------+-------+-----------+-------------+--------------------+-------------+---------+
only showing top 5 rows

Dialogs with Lines

Let's join ght two DataFrames on lineID next.

val convLines = conversations.join(lines, "lineID").sort($"conversationID", $"intraConversationID")
convLines: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [lineID: string, conversationID: bigint ... 7 more fields]
convLines.count
res24: Long = 304713
conversations.count
res25: Long = 304713
display(convLines)
lineID conversationID intraConversationID movieID characterID characterName text movieTitle movieYear
L194 0.0 0.0 m0 u0 BIANCA Can we make this quick? Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad. Again. 10 things i hate about you 1999
L195 0.0 1.0 m0 u2 CAMERON Well, I thought we'd start with pronunciation, if that's okay with you. 10 things i hate about you 1999
L196 0.0 2.0 m0 u0 BIANCA Not the hacking and gagging and spitting part. Please. 10 things i hate about you 1999
L197 0.0 3.0 m0 u2 CAMERON Okay... then how 'bout we try out some French cuisine. Saturday? Night? 10 things i hate about you 1999
L198 1.0 0.0 m0 u0 BIANCA You're asking me out. That's so cute. What's your name again? 10 things i hate about you 1999
L199 1.0 1.0 m0 u2 CAMERON Forget it. 10 things i hate about you 1999
L200 2.0 0.0 m0 u0 BIANCA No, no, it's my fault -- we didn't have a proper introduction --- 10 things i hate about you 1999
L201 2.0 1.0 m0 u2 CAMERON Cameron. 10 things i hate about you 1999
L202 2.0 2.0 m0 u0 BIANCA The thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser. My sister. I can't date until she does. 10 things i hate about you 1999
L203 2.0 3.0 m0 u2 CAMERON Seems like she could get a date easy enough... 10 things i hate about you 1999
L204 3.0 0.0 m0 u2 CAMERON Why? 10 things i hate about you 1999
L205 3.0 1.0 m0 u0 BIANCA Unsolved mystery. She used to be really popular when she started high school, then it was just like she got sick of it or something. 10 things i hate about you 1999
L206 3.0 2.0 m0 u2 CAMERON That's a shame. 10 things i hate about you 1999
L207 4.0 0.0 m0 u0 BIANCA Gosh, if only we could find Kat a boyfriend... 10 things i hate about you 1999
L208 4.0 1.0 m0 u2 CAMERON Let me see what I can do. 10 things i hate about you 1999
L271 5.0 0.0 m0 u0 BIANCA C'esc ma tete. This is my head 10 things i hate about you 1999
L272 5.0 1.0 m0 u2 CAMERON Right. See? You're ready for the quiz. 10 things i hate about you 1999
L273 5.0 2.0 m0 u0 BIANCA I don't want to know how to say that though. I want to know useful things. Like where the good stores are. How much does champagne cost? Stuff like Chat. I have never in my life had to point out my head to someone. 10 things i hate about you 1999
L274 5.0 3.0 m0 u2 CAMERON That's because it's such a nice one. 10 things i hate about you 1999
L275 5.0 4.0 m0 u0 BIANCA Forget French. 10 things i hate about you 1999
L276 6.0 0.0 m0 u0 BIANCA How is our little Find the Wench A Date plan progressing? 10 things i hate about you 1999
L277 6.0 1.0 m0 u2 CAMERON Well, there's someone I think might be -- 10 things i hate about you 1999
L280 7.0 0.0 m0 u2 CAMERON There. 10 things i hate about you 1999
L281 7.0 1.0 m0 u0 BIANCA Where? 10 things i hate about you 1999
L363 8.0 0.0 m0 u2 CAMERON You got something on your mind? 10 things i hate about you 1999
L364 8.0 1.0 m0 u0 BIANCA I counted on you to help my cause. You and that thug are obviously failing. Aren't we ever going on our date? 10 things i hate about you 1999
L365 9.0 0.0 m0 u2 CAMERON You have my word. As a gentleman 10 things i hate about you 1999
L366 9.0 1.0 m0 u0 BIANCA You're sweet. 10 things i hate about you 1999
L367 10.0 0.0 m0 u2 CAMERON How do you get your hair to look like that? 10 things i hate about you 1999
L368 10.0 1.0 m0 u0 BIANCA Eber's Deep Conditioner every two days. And I never, ever use a blowdryer without the diffuser attachment. 10 things i hate about you 1999
L401 11.0 0.0 m0 u2 CAMERON Sure have. 10 things i hate about you 1999
L402 11.0 1.0 m0 u0 BIANCA I really, really, really wanna go, but I can't. Not unless my sister goes. 10 things i hate about you 1999
L403 11.0 2.0 m0 u2 CAMERON I'm workin' on it. But she doesn't seem to be goin' for him. 10 things i hate about you 1999
L404 12.0 0.0 m0 u2 CAMERON She's not a... 10 things i hate about you 1999
L405 12.0 1.0 m0 u0 BIANCA Lesbian? No. I found a picture of Jared Leto in one of her drawers, so I'm pretty sure she's not harboring same-sex tendencies. 10 things i hate about you 1999
L406 12.0 2.0 m0 u2 CAMERON So that's the kind of guy she likes? Pretty ones? 10 things i hate about you 1999
L407 12.0 3.0 m0 u0 BIANCA Who knows? All I've ever heard her say is that she'd dip before dating a guy that smokes. 10 things i hate about you 1999
L575 13.0 0.0 m0 u0 BIANCA Hi. 10 things i hate about you 1999
L576 13.0 1.0 m0 u2 CAMERON Looks like things worked out tonight, huh? 10 things i hate about you 1999
L577 14.0 0.0 m0 u0 BIANCA You know Chastity? 10 things i hate about you 1999
L578 14.0 1.0 m0 u2 CAMERON I believe we share an art instructor 10 things i hate about you 1999
L662 15.0 0.0 m0 u2 CAMERON Have fun tonight? 10 things i hate about you 1999
L663 15.0 1.0 m0 u0 BIANCA Tons 10 things i hate about you 1999
L693 16.0 0.0 m0 u2 CAMERON I looked for you back at the party, but you always seemed to be "occupied". 10 things i hate about you 1999
L694 16.0 1.0 m0 u0 BIANCA I was? 10 things i hate about you 1999
L695 16.0 2.0 m0 u2 CAMERON You never wanted to go out with 'me, did you? 10 things i hate about you 1999
L696 17.0 0.0 m0 u0 BIANCA Well, no... 10 things i hate about you 1999
L697 17.0 1.0 m0 u2 CAMERON Then that's all you had to say. 10 things i hate about you 1999
L698 17.0 2.0 m0 u0 BIANCA But 10 things i hate about you 1999
L699 17.0 3.0 m0 u2 CAMERON You always been this selfish? 10 things i hate about you 1999
L860 18.0 0.0 m0 u0 BIANCA Then Guillermo says, "If you go any lighter, you're gonna look like an extra on 90210." 10 things i hate about you 1999
L861 18.0 1.0 m0 u2 CAMERON No... 10 things i hate about you 1999
L862 19.0 0.0 m0 u0 BIANCA do you listen to this crap? 10 things i hate about you 1999
L863 19.0 1.0 m0 u2 CAMERON What crap? 10 things i hate about you 1999
L864 19.0 2.0 m0 u0 BIANCA Me. This endless ...blonde babble. I'm like, boring myself. 10 things i hate about you 1999
L865 19.0 3.0 m0 u2 CAMERON Thank God! If I had to hear one more story about your coiffure... 10 things i hate about you 1999
L866 20.0 0.0 m0 u2 CAMERON I figured you'd get to the good stuff eventually. 10 things i hate about you 1999
L867 20.0 1.0 m0 u0 BIANCA What good stuff? 10 things i hate about you 1999
L868 20.0 2.0 m0 u2 CAMERON The "real you". 10 things i hate about you 1999
L869 20.0 3.0 m0 u0 BIANCA Like my fear of wearing pastels? 10 things i hate about you 1999
L870 21.0 0.0 m0 u0 BIANCA I'm kidding. You know how sometimes you just become this "persona"? And you don't know how to quit? 10 things i hate about you 1999
L871 21.0 1.0 m0 u2 CAMERON No 10 things i hate about you 1999
L872 21.0 2.0 m0 u0 BIANCA Okay -- you're gonna need to learn how to lie. 10 things i hate about you 1999
L924 22.0 0.0 m0 u2 CAMERON Wow 10 things i hate about you 1999
L925 22.0 1.0 m0 u0 BIANCA Let's go. 10 things i hate about you 1999
L984 23.0 0.0 m0 u2 CAMERON She okay? 10 things i hate about you 1999
L985 23.0 1.0 m0 u0 BIANCA I hope so. 10 things i hate about you 1999
L1044 24.0 0.0 m0 u2 CAMERON They do to! 10 things i hate about you 1999
L1045 24.0 1.0 m0 u0 BIANCA They do not! 10 things i hate about you 1999
L49 25.0 0.0 m0 u0 BIANCA Did you change your hair? 10 things i hate about you 1999
L50 25.0 1.0 m0 u3 CHASTITY No. 10 things i hate about you 1999
L51 25.0 2.0 m0 u0 BIANCA You might wanna think about it 10 things i hate about you 1999
L571 26.0 0.0 m0 u0 BIANCA Where did he go? He was just here. 10 things i hate about you 1999
L572 26.0 1.0 m0 u3 CHASTITY Who? 10 things i hate about you 1999
L573 26.0 2.0 m0 u0 BIANCA Joey. 10 things i hate about you 1999
L579 27.0 0.0 m0 u3 CHASTITY Great 10 things i hate about you 1999
L580 27.0 1.0 m0 u0 BIANCA Would you mind getting me a drink, Cameron? 10 things i hate about you 1999
L595 28.0 0.0 m0 u0 BIANCA He practically proposed when he found out we had the same dermatologist. I mean. Dr. Bonchowski is great an all, but he's not exactly relevant party conversation. 10 things i hate about you 1999
L596 28.0 1.0 m0 u3 CHASTITY Is he oily or dry? 10 things i hate about you 1999
L597 28.0 2.0 m0 u0 BIANCA Combination. I don't know -- I thought he'd be different. More of a gentleman... 10 things i hate about you 1999
L598 29.0 0.0 m0 u3 CHASTITY Bianca, I don't think the highlights of dating Joey Dorsey are going to include door-opening and coat-holding. 10 things i hate about you 1999
L599 29.0 1.0 m0 u0 BIANCA Sometimes I wonder if the guys we're supposed to want to go out with are the ones we actually want to go out with, you know? 10 things i hate about you 1999
L600 29.0 2.0 m0 u3 CHASTITY All I know is -- I'd give up my private line to go out with a guy like Joey. 10 things i hate about you 1999
L659 30.0 0.0 m0 u0 BIANCA I have to be home in twenty minutes. 10 things i hate about you 1999
L660 30.0 1.0 m0 u3 CHASTITY I don't have to be home 'til two. 10 things i hate about you 1999
L952 31.0 0.0 m0 u3 CHASTITY You think you ' re the only sophomore at the prom? 10 things i hate about you 1999
L953 31.0 1.0 m0 u0 BIANCA I did. 10 things i hate about you 1999
L394 32.0 0.0 m0 u4 JOEY It's more 10 things i hate about you 1999
L395 32.0 1.0 m0 u0 BIANCA Expensive? 10 things i hate about you 1999
L396 33.0 0.0 m0 u4 JOEY Exactly So, you going to Bogey Lowenbrau's thing on Saturday? 10 things i hate about you 1999
L397 33.0 1.0 m0 u0 BIANCA Hopefully. 10 things i hate about you 1999
L589 34.0 0.0 m0 u4 JOEY So yeah, I've got the Sears catalog thing going -- and the tube sock gig " that's gonna be huge. And then I'm up for an ad for Queen Harry next week. 10 things i hate about you 1999
L590 34.0 1.0 m0 u0 BIANCA Queen Harry? 10 things i hate about you 1999
L591 34.0 2.0 m0 u4 JOEY It's a gay cruise line, but I'll be, like, wearing a uniform and stuff. 10 things i hate about you 1999
L592 35.0 0.0 m0 u0 BIANCA Neat... 10 things i hate about you 1999
L593 35.0 1.0 m0 u4 JOEY My agent says I've got a good shot at being the Prada guy next year. 10 things i hate about you 1999
L756 36.0 0.0 m0 u4 JOEY Hey, sweet cheeks. 10 things i hate about you 1999
L757 36.0 1.0 m0 u0 BIANCA Hi, Joey. 10 things i hate about you 1999
L758 36.0 2.0 m0 u4 JOEY You're concentrating awfully hard considering it's gym class. 10 things i hate about you 1999
L759 37.0 0.0 m0 u4 JOEY Listen, I want to talk to you about the prom. 10 things i hate about you 1999
L760 37.0 1.0 m0 u0 BIANCA You know the deal. I can ' t go if Kat doesn't go -- 10 things i hate about you 1999
L164 38.0 0.0 m0 u5 KAT Where've you been? 10 things i hate about you 1999
L165 38.0 1.0 m0 u0 BIANCA Nowhere... Hi, Daddy. 10 things i hate about you 1999
L319 39.0 0.0 m0 u5 KAT I have the potential to smack the crap out of you if you don't get out of my way. 10 things i hate about you 1999
L320 39.0 1.0 m0 u0 BIANCA Can you at least start wearing a bra? 10 things i hate about you 1999
L441 40.0 0.0 m0 u0 BIANCA Oh my God, does this mean you're becoming normal? 10 things i hate about you 1999
L442 40.0 1.0 m0 u5 KAT It means that Gigglepuss is playing at Club Skunk and we're going. 10 things i hate about you 1999
L443 40.0 2.0 m0 u0 BIANCA Oh, I thought you might have a date I don't know why I'm bothering to ask, but are you going to Bogey Lowenstein's party Saturday night? 10 things i hate about you 1999
L444 40.0 3.0 m0 u5 KAT What do you think? 10 things i hate about you 1999
L445 40.0 4.0 m0 u0 BIANCA I think you're a freak. I think you do this to torture me. And I think you suck. 10 things i hate about you 1999
L525 41.0 0.0 m0 u0 BIANCA You're ruining my life' Because you won't be normal, I can't be normal. 10 things i hate about you 1999
L526 41.0 1.0 m0 u5 KAT What's normal? 10 things i hate about you 1999
L527 41.0 2.0 m0 u0 BIANCA Bogey Lowenstein's party is normal, but you're too busy listening to Bitches Who Need Prozac to know that. 10 things i hate about you 1999
L529 42.0 0.0 m0 u0 BIANCA Can't you forget for just one night that you're completely wretched? 10 things i hate about you 1999
L530 42.0 1.0 m0 u5 KAT At least I'm not a clouted fen- sucked hedge-pig. 10 things i hate about you 1999
L531 43.0 0.0 m0 u0 BIANCA Like I'm supposed to know what that even means. 10 things i hate about you 1999
L532 43.0 1.0 m0 u5 KAT It's Shakespeare. Maybe you've heard of him? 10 things i hate about you 1999
L533 43.0 2.0 m0 u0 BIANCA Yeah, he's your freak friend Mandella's boyfriend. I guess since I'm not allowed to go out, I should obsess over a dead guy, too. 10 things i hate about you 1999
L542 44.0 0.0 m0 u0 BIANCA You are so completely unbalanced. 10 things i hate about you 1999
L543 44.0 1.0 m0 u5 KAT Can we go now? 10 things i hate about you 1999
L601 45.0 0.0 m0 u5 KAT Bianca, I need to talk to you -- I need to tell you -- 10 things i hate about you 1999
L602 45.0 1.0 m0 u0 BIANCA I really don't think I need any social advice from you right now. 10 things i hate about you 1999
L655 46.0 0.0 m0 u0 BIANCA I don't get you. You act like you're too good for any of this, and then you go totally apeshit when you get here. 10 things i hate about you 1999
L656 46.0 1.0 m0 u5 KAT You're welcome. 10 things i hate about you 1999
L889 47.0 0.0 m0 u5 KAT Listen, I know you hate having to sit home because I'm not Susie High School. 10 things i hate about you 1999
L890 47.0 1.0 m0 u0 BIANCA Like you care. 10 things i hate about you 1999
L891 47.0 2.0 m0 u5 KAT I do care. But I'm a firm believer in doing something for your own reasons, not someone else ' s . 10 things i hate about you 1999
L892 47.0 3.0 m0 u0 BIANCA I wish I had that luxury. I'm the only sophomore that got asked to the prom and I can't go, because you won ' t. 10 things i hate about you 1999
L893 48.0 0.0 m0 u5 KAT Joey never told you we went out, did he? 10 things i hate about you 1999
L894 48.0 1.0 m0 u0 BIANCA What? 10 things i hate about you 1999
L895 48.0 2.0 m0 u5 KAT In 9th. For a month 10 things i hate about you 1999
L896 48.0 3.0 m0 u0 BIANCA Why? 10 things i hate about you 1999
L897 48.0 4.0 m0 u5 KAT He was, like, a total babe 10 things i hate about you 1999
L898 48.0 5.0 m0 u0 BIANCA But you hate Joey 10 things i hate about you 1999
L899 48.0 6.0 m0 u5 KAT Now I do. Back then, was a different story. 10 things i hate about you 1999
L900 48.0 7.0 m0 u0 BIANCA As in... 10 things i hate about you 1999
L901 49.0 0.0 m0 u5 KAT He said everyone was doing it. So I did it. 10 things i hate about you 1999
L902 49.0 1.0 m0 u0 BIANCA You did what? 10 things i hate about you 1999
L903 49.0 2.0 m0 u5 KAT Just once. Afterwards, I told him I didn't want to anymore. I wasn't ready. He got pissed. Then he broke up with me. 10 things i hate about you 1999
L904 50.0 0.0 m0 u0 BIANCA But 10 things i hate about you 1999
L905 50.0 1.0 m0 u5 KAT After that, I swore I'd never do anything just because "everyone else" was doing it. And I haven't since. Except for Bogey's party, and my stunning gastro-intestinal display -- 10 things i hate about you 1999
L906 50.0 2.0 m0 u0 BIANCA Why didn't you tell me? 10 things i hate about you 1999
L907 50.0 3.0 m0 u5 KAT I wanted to let you make up your own mind about him. 10 things i hate about you 1999
L908 50.0 4.0 m0 u0 BIANCA No. you didn't! If you really thought I could make my own decisions, you would've let me go out with him instead of helping Daddy hold me hostage. 10 things i hate about you 1999
L909 51.0 0.0 m0 u5 KAT That's not 10 things i hate about you 1999
L910 51.0 1.0 m0 u0 BIANCA I'm not stupid enough to repeat your mistakes. 10 things i hate about you 1999
L911 51.0 2.0 m0 u5 KAT I guess I thought I was protecting you. 10 things i hate about you 1999
L912 51.0 3.0 m0 u0 BIANCA God, you're just like him! Just keep me locked away in the dark, so I can't experience anything for myself 10 things i hate about you 1999
L913 51.0 4.0 m0 u5 KAT Not all experiences are good, Bianca. You can't always trust the people you want to. 10 things i hate about you 1999
L914 51.0 5.0 m0 u0 BIANCA I guess I'll never know, will I? 10 things i hate about you 1999
L982 52.0 0.0 m0 u0 BIANCA You looked beautiful last night, you know. 10 things i hate about you 1999
L983 52.0 1.0 m0 u5 KAT So did you 10 things i hate about you 1999
L1007 53.0 0.0 m0 u0 BIANCA Let go! 10 things i hate about you 1999
L1008 53.0 1.0 m0 u5 KAT You set me up. 10 things i hate about you 1999
L1009 53.0 2.0 m0 u0 BIANCA I just wanted -- 10 things i hate about you 1999
L1010 53.0 3.0 m0 u5 KAT What? To completely damage me? To send me to therapy forever? What? 10 things i hate about you 1999
L1011 53.0 4.0 m0 u0 BIANCA No! I just wanted 10 things i hate about you 1999
L1021 54.0 0.0 m0 u0 BIANCA Is that woman a complete fruit-loop or is it just me? 10 things i hate about you 1999
L1022 54.0 1.0 m0 u5 KAT It's just you. 10 things i hate about you 1999
L1051 55.0 0.0 m0 u0 BIANCA Patrick -- is that- a. 10 things i hate about you 1999
L1052 55.0 1.0 m0 u5 KAT Perm? 10 things i hate about you 1999
L179 56.0 0.0 m0 u0 BIANCA Now don't get upset. Daddy, but there's this boy... and I think he might ask... 10 things i hate about you 1999
L180 56.0 1.0 m0 u11 WALTER No! You're not dating until your sister starts dating. End of discussion. 10 things i hate about you 1999
L181 56.0 2.0 m0 u0 BIANCA What if she never starts dating? 10 things i hate about you 1999
L182 56.0 3.0 m0 u11 WALTER Then neither will you. And I'll get to sleep at night. 10 things i hate about you 1999
L183 56.0 4.0 m0 u0 BIANCA But it's not fair -- she's a mutant, Daddy! 10 things i hate about you 1999
L189 57.0 0.0 m0 u0 BIANCA But she doesn't want to date. 10 things i hate about you 1999
L190 57.0 1.0 m0 u11 WALTER Exactly my point 10 things i hate about you 1999
L517 58.0 0.0 m0 u0 BIANCA Daddy, I -- 10 things i hate about you 1999
L518 58.0 1.0 m0 u11 WALTER And where're you going? 10 things i hate about you 1999
L519 58.0 2.0 m0 u0 BIANCA If you must know, we were attempting to go to a small study group of friends. 10 things i hate about you 1999
L520 58.0 3.0 m0 u11 WALTER Otherwise known as an orgy? 10 things i hate about you 1999
L521 58.0 4.0 m0 u0 BIANCA It's just a party. Daddy, but I knew you'd forbid me to go since "Gloria Steinem" over there isn't going -- 10 things i hate about you 1999
L523 59.0 0.0 m0 u0 BIANCA Daddy, people expect me to be there! 10 things i hate about you 1999
L524 59.0 1.0 m0 u11 WALTER If Kat's not going, you're not going. 10 things i hate about you 1999
L536 60.0 0.0 m0 u11 WALTER Oh, God. It's starting. 10 things i hate about you 1999
L537 60.0 1.0 m0 u0 BIANCA It's just a party. Daddy. 10 things i hate about you 1999
L538 61.0 0.0 m0 u11 WALTER Wear the belly before you go. 10 things i hate about you 1999
L539 61.0 1.0 m0 u0 BIANCA Daddy, no! 10 things i hate about you 1999
L540 61.0 2.0 m0 u11 WALTER Just for a minute 10 things i hate about you 1999
L544 62.0 0.0 m0 u11 WALTER Promise me you won't talk to any boys unless your sister is present. 10 things i hate about you 1999
L545 62.0 1.0 m0 u0 BIANCA Why? 10 things i hate about you 1999
L546 62.0 2.0 m0 u11 WALTER Because she'll scare them away. 10 things i hate about you 1999
L878 63.0 0.0 m0 u0 BIANCA Daddy, I want to discuss the prom with you. It's tomorrow night -- 10 things i hate about you 1999
L879 63.0 1.0 m0 u11 WALTER The prom? Kat has a date? 10 things i hate about you 1999
L880 63.0 2.0 m0 u0 BIANCA No, but 10 things i hate about you 1999
L881 63.0 3.0 m0 u11 WALTER It's that hot rod Joey, right? That ' s who you want me to bend my rules for? 10 things i hate about you 1999
L882 63.0 4.0 m0 u0 BIANCA He's not a "hot rod". Whatever that is. 10 things i hate about you 1999
L883 63.0 5.0 m0 u11 WALTER You're not going unless your sister goes. End of story. 10 things i hate about you 1999
L884 63.0 6.0 m0 u0 BIANCA Fine. I see that I'm a prisoner in my own house. I'm not a daughter. I'm a possession! 10 things i hate about you 1999
L922 64.0 0.0 m0 u11 WALTER I'm missing something. 10 things i hate about you 1999
L923 64.0 1.0 m0 u0 BIANCA I have a date, Daddy. And he ' s not a captain of oppression like some men we know. 10 things i hate about you 1999
L458 65.0 0.0 m0 u9 PATRICK Always a pleasure, Brucie. 10 things i hate about you 1999
L459 65.0 1.0 m0 u1 BRUCE Didn't have you pegged for a Gigglepuss fan. Aren't they a little too pre-teen belly-button ring for you? 10 things i hate about you 1999
L460 65.0 2.0 m0 u9 PATRICK Fan of a fan. You see a couple of minors come in? 10 things i hate about you 1999
L461 65.0 3.0 m0 u1 BRUCE Never 10 things i hate about you 1999
L462 65.0 4.0 m0 u9 PATRICK Padua girls. One tall, decent body. The other one kinda short and undersexed? 10 things i hate about you 1999
L463 65.0 5.0 m0 u1 BRUCE Just sent 'em through. 10 things i hate about you 1999
L63 66.0 0.0 m0 u7 MICHAEL You the new guy? 10 things i hate about you 1999
L64 66.0 1.0 m0 u2 CAMERON So they tell me... 10 things i hate about you 1999
L65 66.0 2.0 m0 u7 MICHAEL C'mon. I'm supposed to give you the tour. 10 things i hate about you 1999
L66 67.0 0.0 m0 u7 MICHAEL So -- which Dakota you from? 10 things i hate about you 1999
L67 67.0 1.0 m0 u2 CAMERON North, actually. How'd you ? 10 things i hate about you 1999
L68 67.0 2.0 m0 u7 MICHAEL I was kidding. People actually live there? 10 things i hate about you 1999
L69 67.0 3.0 m0 u2 CAMERON Yeah. A couple. We're outnumbered by the cows, though. 10 things i hate about you 1999
L70 67.0 4.0 m0 u7 MICHAEL How many people were in your old school? 10 things i hate about you 1999
L71 67.0 5.0 m0 u2 CAMERON Thirty-two. 10 things i hate about you 1999
L72 67.0 6.0 m0 u7 MICHAEL Get out! 10 things i hate about you 1999
L73 67.0 7.0 m0 u2 CAMERON How many people go here? 10 things i hate about you 1999
L74 67.0 8.0 m0 u7 MICHAEL Couple thousand. Most of them evil 10 things i hate about you 1999
L77 68.0 0.0 m0 u2 CAMERON That I'm used to. 10 things i hate about you 1999
L78 68.0 1.0 m0 u7 MICHAEL Yeah, but these guys have never seen a horse. They just jack off to Clint Eastwood. 10 things i hate about you 1999
L87 69.0 0.0 m0 u2 CAMERON That girl -- I -- 10 things i hate about you 1999
L88 69.0 1.0 m0 u7 MICHAEL You burn, you pine, you perish? 10 things i hate about you 1999
L89 69.0 2.0 m0 u2 CAMERON Who is she? 10 things i hate about you 1999
L90 69.0 3.0 m0 u7 MICHAEL Bianca Stratford. Sophomore. Don't even think about it 10 things i hate about you 1999
L91 69.0 4.0 m0 u2 CAMERON Why not? 10 things i hate about you 1999
L92 69.0 5.0 m0 u7 MICHAEL I could start with your haircut, but it doesn't matter. She's not allowed to date until her older sister does. And that's an impossibility. 10 things i hate about you 1999
L139 70.0 0.0 m0 u2 CAMERON Why do girls like that always like guys like that? 10 things i hate about you 1999
L140 70.0 1.0 m0 u7 MICHAEL Because they're bred to. Their mothers liked guys like that, and their grandmothers before them. Their gene pool is rarely diluted. 10 things i hate about you 1999
L141 70.0 2.0 m0 u2 CAMERON He always have that shit-eating grin? 10 things i hate about you 1999
L142 70.0 3.0 m0 u7 MICHAEL Joey Dorsey? Perma-shit-grin. I wish I could say he's a moron, but he's number twelve in the class. And a model. Mostly regional stuff, but he's rumored to have a big tube sock ad coming out. 10 things i hate about you 1999
L143 71.0 0.0 m0 u7 MICHAEL You know French? 10 things i hate about you 1999
L144 71.0 1.0 m0 u2 CAMERON Sure do ... my Mom's from Canada 10 things i hate about you 1999
L145 71.0 2.0 m0 u7 MICHAEL Guess who just signed up for a tutor? 10 things i hate about you 1999
L146 71.0 3.0 m0 u2 CAMERON You mean I'd get a chance to talk to her? 10 things i hate about you 1999
L147 71.0 4.0 m0 u7 MICHAEL You could consecrate with her, my friend. 10 things i hate about you 1999
L157 72.0 0.0 m0 u7 MICHAEL Yeah, just a minor encounter with the shrew. 10 things i hate about you 1999
L158 72.0 1.0 m0 u2 CAMERON That's her? Bianca's sister? 10 things i hate about you 1999
L159 72.0 2.0 m0 u7 MICHAEL The mewling, rampalian wretch herself. 10 things i hate about you 1999
L210 73.0 0.0 m0 u2 CAMERON I teach her French, get to know her, dazzle her with charm and she falls in love with me. 10 things i hate about you 1999
L211 73.0 1.0 m0 u7 MICHAEL Unlikely, but even so, she still can't go out with you. So what's the point? 10 things i hate about you 1999
L212 74.0 0.0 m0 u2 CAMERON What about him? 10 things i hate about you 1999
L213 74.0 1.0 m0 u7 MICHAEL You wanna go out with him? 10 things i hate about you 1999
L215 75.0 0.0 m0 u7 MICHAEL What makes you think he'll do it? 10 things i hate about you 1999
L216 75.0 1.0 m0 u2 CAMERON He seems like he thrives on danger 10 things i hate about you 1999
L217 75.0 2.0 m0 u7 MICHAEL No kidding. He's a criminal. I heard he lit a state trooper on fire. He just got out of Alcatraz... 10 things i hate about you 1999
L218 75.0 3.0 m0 u2 CAMERON They always let felons sit in on Honors Biology? 10 things i hate about you 1999
L219 75.0 4.0 m0 u7 MICHAEL I'm serious, man, he's whacked. He sold his own liver on the black market so he could buy new speakers. 10 things i hate about you 1999
L220 75.0 5.0 m0 u2 CAMERON Forget his reputation. Do you think we've got a plan or not? 10 things i hate about you 1999
L221 75.0 6.0 m0 u7 MICHAEL Did she actually say she'd go out with you? 10 things i hate about you 1999
L222 75.0 7.0 m0 u2 CAMERON That's what I just said 10 things i hate about you 1999
L223 76.0 0.0 m0 u7 MICHAEL You know, if you do go out with Bianca, you'd be set. You'd outrank everyone. Strictly A-list. With me by your side. 10 things i hate about you 1999
L224 76.0 1.0 m0 u2 CAMERON I thought you hated those people. 10 things i hate about you 1999
L225 76.0 2.0 m0 u7 MICHAEL Hey -- I've gotta have a few clients when I get to Wall Street. 10 things i hate about you 1999
L244 77.0 0.0 m0 u2 CAMERON You got him involved? 10 things i hate about you 1999
L245 77.0 1.0 m0 u7 MICHAEL Like we had a choice? Besides -- when you let the enemy think he's orchestrating the battle, you're in a position of power. We let him pretend he's calling the shots, and while he's busy setting up the plan, you have time to woo Bianca. 10 things i hate about you 1999
L388 78.0 0.0 m0 u2 CAMERON This is it. A golden opportunity. Patrick can ask Katarina to the party. 10 things i hate about you 1999
L389 78.0 1.0 m0 u7 MICHAEL In that case, we'll need to make it a school-wide blow out. 10 things i hate about you 1999
L390 78.0 2.0 m0 u2 CAMERON Will Bogey get bent? 10 things i hate about you 1999
L391 78.0 3.0 m0 u7 MICHAEL Are you kidding? He'll piss himself with joy. He's the ultimate kiss ass. 10 things i hate about you 1999
L416 79.0 0.0 m0 u2 CAMERON Number one. She hates smokers 10 things i hate about you 1999
L417 79.0 1.0 m0 u7 MICHAEL It's a lung cancer issue 10 things i hate about you 1999
L418 79.0 2.0 m0 u2 CAMERON Her favorite uncle 10 things i hate about you 1999
L419 79.0 3.0 m0 u7 MICHAEL Dead at forty-one. 10 things i hate about you 1999
L424 80.0 0.0 m0 u7 MICHAEL He's pretty! 10 things i hate about you 1999
L425 80.0 1.0 m0 u2 CAMERON Okay! I wasn't sure 10 things i hate about you 1999
L432 81.0 0.0 m0 u7 MICHAEL Assail your ears for one night. 10 things i hate about you 1999
L433 81.0 1.0 m0 u2 CAMERON It's her favorite band. 10 things i hate about you 1999
L499 82.0 0.0 m0 u7 MICHAEL You told me that part already. 10 things i hate about you 1999
L500 82.0 1.0 m0 u2 CAMERON Hell, I've just been going over the whole thing in my head and - 10 things i hate about you 1999
L583 83.0 0.0 m0 u7 MICHAEL Extremely unfortunate maneuver. 10 things i hate about you 1999
L584 83.0 1.0 m0 u2 CAMERON The hell is that? What kind of 'guy just picks up a girl and carries her away while you're talking to her? 10 things i hate about you 1999
L585 83.0 2.0 m0 u7 MICHAEL Buttholus extremus. But hey, you're making progress. 10 things i hate about you 1999
L586 83.0 3.0 m0 u2 CAMERON No, I ' m not. 10 things i hate about you 1999
L778 84.0 0.0 m0 u2 CAMERON You humiliated the woman! Sacrifice yourself on the altar of dignity and even the score. 10 things i hate about you 1999
L779 84.0 1.0 m0 u7 MICHAEL Best case scenario, you're back on the payroll for awhile. 10 things i hate about you 1999
L385 85.0 0.0 m0 u2 CAMERON And he means that strictly in a non- prison-movie type of way. 10 things i hate about you 1999
L386 85.0 1.0 m0 u9 PATRICK Yeah -- we'll see. 10 things i hate about you 1999
L411 86.0 0.0 m0 u9 PATRICK What've you got for me? 10 things i hate about you 1999
L412 86.0 1.0 m0 u2 CAMERON I've retrieved certain pieces of information on Miss Katarina Stratford I think you'll find helpful. 10 things i hate about you 1999
L426 87.0 0.0 m0 u2 CAMERON Okay -- Likes: Thai food, feminist prose, and "angry, stinky girl music of the indie-rock persuasion". 10 things i hate about you 1999
L427 87.0 1.0 m0 u9 PATRICK So what does that give me? I'm supposed to buy her some noodles and a book and sit around listening to chicks who can't play their instruments? 10 things i hate about you 1999
L430 88.0 0.0 m0 u2 CAMERON Gigglepuss is playing there tomorrow night. 10 things i hate about you 1999
L431 88.0 1.0 m0 u9 PATRICK Don't make me do it, man 10 things i hate about you 1999
L619 89.0 0.0 m0 u9 PATRICK Cameron, I'm a little busy 10 things i hate about you 1999
L620 89.0 1.0 m0 u2 CAMERON It's off. The whole thing. 10 things i hate about you 1999
L621 90.0 0.0 m0 u9 PATRICK What 're you talking about? 10 things i hate about you 1999
L622 90.0 1.0 m0 u2 CAMERON She's partial to Joey, not me 10 things i hate about you 1999
L623 91.0 0.0 m0 u9 PATRICK Cameron -- do you like the girl? 10 things i hate about you 1999
L624 91.0 1.0 m0 u2 CAMERON Sure 10 things i hate about you 1999
L625 91.0 2.0 m0 u9 PATRICK Then, go get her 10 things i hate about you 1999
L725 92.0 0.0 m0 u2 CAMERON What'd you do to her? 10 things i hate about you 1999
L726 92.0 1.0 m0 u9 PATRICK I don ' t know. I decided not to nail her when she was too drunk to remember it. 10 things i hate about you 1999
L740 93.0 0.0 m0 u2 CAMERON She hates you with the fire of a thousand suns . That's a direct quote 10 things i hate about you 1999
L741 93.0 1.0 m0 u9 PATRICK She just needs time to cool off I'll give it a day. 10 things i hate about you 1999
L743 94.0 0.0 m0 u9 PATRICK You makin' any headway? 10 things i hate about you 1999
L744 94.0 1.0 m0 u2 CAMERON She kissed me. 10 things i hate about you 1999
L745 94.0 2.0 m0 u9 PATRICK Where? 10 things i hate about you 1999
L780 95.0 0.0 m0 u9 PATRICK What's the worst? 10 things i hate about you 1999
L781 95.0 1.0 m0 u2 CAMERON You get the girl. 10 things i hate about you 1999
L148 96.0 0.0 m0 u4 JOEY The vintage look is over, Kat. Haven't you been reading your Sassy? 10 things i hate about you 1999
L149 96.0 1.0 m0 u5 KAT Yeah, and I noticed the only part of you featured in your big Kmart spread was your elbow. Tough break. 10 things i hate about you 1999
L150 96.0 2.0 m0 u4 JOEY They're running the rest of me next month. 10 things i hate about you 1999
L335 97.0 0.0 m0 u5 KAT Hey -- do you mind? 10 things i hate about you 1999
L336 97.0 1.0 m0 u4 JOEY Not at all 10 things i hate about you 1999
L554 98.0 0.0 m0 u4 JOEY Where ya goin? 10 things i hate about you 1999
L555 98.0 1.0 m0 u5 KAT Away. 10 things i hate about you 1999
L556 98.0 2.0 m0 u4 JOEY Your sister here? 10 things i hate about you 1999
L557 99.0 0.0 m0 u5 KAT Leave my sister alone. 10 things i hate about you 1999
L558 99.0 1.0 m0 u4 JOEY And why would I do that? 10 things i hate about you 1999
L285 100.0 0.0 m0 u9 PATRICK Yeah 10 things i hate about you 1999
L286 100.0 1.0 m0 u4 JOEY What do you think? 10 things i hate about you 1999
L287 101.0 0.0 m0 u9 PATRICK Two legs, nice rack... 10 things i hate about you 1999
L288 101.0 1.0 m0 u4 JOEY Yeah, whatever. I want you to go out with her. 10 things i hate about you 1999
L289 101.0 2.0 m0 u9 PATRICK Sure, Sparky. I'll get right on it. 10 things i hate about you 1999
L290 101.0 3.0 m0 u4 JOEY You just said 10 things i hate about you 1999
L291 101.0 4.0 m0 u9 PATRICK You need money to take a girl out 10 things i hate about you 1999
L292 101.0 5.0 m0 u4 JOEY But you'd go out with her if you had the cake? 10 things i hate about you 1999
L294 102.0 0.0 m0 u4 JOEY You got it, Verona. I pick up the tab, you do the honors. 10 things i hate about you 1999
L295 102.0 1.0 m0 u9 PATRICK You're gonna pay me to take out some girl? 10 things i hate about you 1999
L296 102.0 2.0 m0 u4 JOEY I can't date her sister until that one gets a boyfriend. And that's the catch. She doesn't want a boyfriend. 10 things i hate about you 1999
L297 102.0 3.0 m0 u9 PATRICK How much? 10 things i hate about you 1999
L298 103.0 0.0 m0 u9 PATRICK I can't take a girl like that out on twenty bucks. 10 things i hate about you 1999
L299 103.0 1.0 m0 u4 JOEY Fine, thirty. 10 things i hate about you 1999
L300 104.0 0.0 m0 u4 JOEY Take it or leave it. This isn't a negotiation. 10 things i hate about you 1999
L301 104.0 1.0 m0 u9 PATRICK Fifty, and you've got your man. 10 things i hate about you 1999
L349 105.0 0.0 m0 u4 JOEY When I shell out fifty, I expect results. 10 things i hate about you 1999
L350 105.0 1.0 m0 u9 PATRICK I'm on it 10 things i hate about you 1999
L351 105.0 2.0 m0 u4 JOEY Watching the bitch trash my car doesn't count as a date. 10 things i hate about you 1999
L352 105.0 3.0 m0 u9 PATRICK I got her under control. She just acts crazed in public to keep up the image. 10 things i hate about you 1999
L354 106.0 0.0 m0 u9 PATRICK I just upped my price 10 things i hate about you 1999
L355 106.0 1.0 m0 u4 JOEY What? 10 things i hate about you 1999
L356 106.0 2.0 m0 u9 PATRICK A hundred bucks a date. 10 things i hate about you 1999
L357 106.0 3.0 m0 u4 JOEY Forget it. 10 things i hate about you 1999
L358 106.0 4.0 m0 u9 PATRICK Forget her sister, then. 10 things i hate about you 1999
L605 107.0 0.0 m0 u9 PATRICK It's about time. 10 things i hate about you 1999
L606 107.0 1.0 m0 u4 JOEY A deal's a deal. 10 things i hate about you 1999
L607 108.0 0.0 m0 u4 JOEY How'd you do it? 10 things i hate about you 1999
L608 108.0 1.0 m0 u9 PATRICK Do what? 10 things i hate about you 1999
L609 108.0 2.0 m0 u4 JOEY Get her to act like a human 10 things i hate about you 1999
L747 109.0 0.0 m0 u9 PATRICK I don't know, Dorsey. ..the limo.-the flowers. Another hundred for the tux -- 10 things i hate about you 1999
L748 109.0 1.0 m0 u4 JOEY Enough with the Barbie n' Ken shit. I know. 10 things i hate about you 1999
L231 110.0 0.0 m0 u7 MICHAEL Hey. 10 things i hate about you 1999
L232 110.0 1.0 m0 u4 JOEY Are you lost? 10 things i hate about you 1999
L233 110.0 2.0 m0 u7 MICHAEL Nope - just came by to chat 10 things i hate about you 1999
L234 110.0 3.0 m0 u4 JOEY We don't chat. 10 things i hate about you 1999
L235 110.0 4.0 m0 u7 MICHAEL Well, actually, I thought I'd run an idea by you. You know, just to see if you're interested. 10 things i hate about you 1999
L236 110.0 5.0 m0 u4 JOEY We're not. 10 things i hate about you 1999
L238 111.0 0.0 m0 u7 MICHAEL But she can't go out with you because her sister is this insane head case and no one will go out with her. right? 10 things i hate about you 1999
L239 111.0 1.0 m0 u4 JOEY Does this conversation have a purpose? 10 things i hate about you 1999
L240 111.0 2.0 m0 u7 MICHAEL So what you need to do is recruit a guy who'll go out with her. Someone who's up for the job. 10 things i hate about you 1999
L502 112.0 0.0 m0 u4 JOEY I hear you're helpin' Verona. 10 things i hate about you 1999
L503 112.0 1.0 m0 u7 MICHAEL Uh, yeah. We're old friend* 10 things i hate about you 1999
L504 112.0 2.0 m0 u4 JOEY You and Verona? 10 things i hate about you 1999
L505 112.0 3.0 m0 u7 MICHAEL What? We took bathes together when we were kids. 10 things i hate about you 1999
L508 113.0 0.0 m0 u4 JOEY You better not fuck this up. I'm heavily invested. 10 things i hate about you 1999
L509 113.0 1.0 m0 u7 MICHAEL Hey -- it's all for the higher good right? 10 things i hate about you 1999
L123 114.0 0.0 m0 u6 MANDELLA Who's that? 10 things i hate about you 1999
L124 114.0 1.0 m0 u5 KAT Patrick Verona Random skid. 10 things i hate about you 1999
L125 114.0 2.0 m0 u6 MANDELLA That's Pat Verona? The one who was gone for a year? I heard he was doing porn movies. 10 things i hate about you 1999
L126 114.0 3.0 m0 u5 KAT I'm sure he's completely incapable of doing anything that interesting. 10 things i hate about you 1999
L127 114.0 4.0 m0 u6 MANDELLA He always look so 10 things i hate about you 1999
L128 114.0 5.0 m0 u5 KAT Block E? 10 things i hate about you 1999
L129 115.0 0.0 m0 u5 KAT Mandella, eat. Starving yourself is a very slow way to die. 10 things i hate about you 1999
L130 115.0 1.0 m0 u6 MANDELLA Just a little. 10 things i hate about you 1999
L131 116.0 0.0 m0 u5 KAT What's this? 10 things i hate about you 1999
L132 116.0 1.0 m0 u6 MANDELLA An attempted slit. 10 things i hate about you 1999
L133 117.0 0.0 m0 u5 KAT I realize that the men of this fine institution are severely lacking, but killing yourself so you can be with William Shakespeare is beyond the scope of normal teenage obsessions. You're venturing far past daytime talk show fodder and entering the world of those who need very expensive therapy. 10 things i hate about you 1999
L134 117.0 1.0 m0 u6 MANDELLA But imagine the things he'd say during sex. 10 things i hate about you 1999
L151 118.0 0.0 m0 u5 KAT The people at this school are so incredibly foul. 10 things i hate about you 1999
L152 118.0 1.0 m0 u6 MANDELLA You could always go with me. I'm sure William has some friends. 10 things i hate about you 1999
L247 119.0 0.0 m0 u5 KAT So he has this huge raging fit about Sarah Lawrence and insists that I go to his male-dominated, puking frat boy, number one golf team school. I have no say at all. 10 things i hate about you 1999
L248 119.0 1.0 m0 u6 MANDELLA William would never have gone to a state school. 10 things i hate about you 1999
L249 119.0 2.0 m0 u5 KAT William didn't even go to high school 10 things i hate about you 1999
L250 119.0 3.0 m0 u6 MANDELLA That's never been proven 10 things i hate about you 1999
L251 119.0 4.0 m0 u5 KAT Neither has his heterosexuality. 10 things i hate about you 1999
L252 120.0 0.0 m0 u5 KAT I appreciate your efforts toward a speedy death, but I'm consuming. Do you mind? 10 things i hate about you 1999
L253 120.0 1.0 m0 u6 MANDELLA Does it matter? 10 things i hate about you 1999
L254 120.0 2.0 m0 u5 KAT If I was Bianca, it would be, "Any school you want, precious. Don't forget your tiara." 10 things i hate about you 1999
L447 121.0 0.0 m0 u6 MANDELLA You think this'll work? 10 things i hate about you 1999
L448 121.0 1.0 m0 u5 KAT No fear. 10 things i hate about you 1999
L490 122.0 0.0 m0 u6 MANDELLA What'd he say? 10 things i hate about you 1999
L491 122.0 1.0 m0 u5 KAT Who cares? 10 things i hate about you 1999
L716 123.0 0.0 m0 u6 MANDELLA You went to the party? I thought we were officially opposed to suburban social activity. 10 things i hate about you 1999
L717 123.0 1.0 m0 u5 KAT I didn't have a choice. 10 things i hate about you 1999
L718 123.0 2.0 m0 u6 MANDELLA You didn't have a choice? Where's Kat and what have you done with her? 10 things i hate about you 1999
L719 123.0 3.0 m0 u5 KAT I did Bianca a favor and it backfired. 10 things i hate about you 1999
L720 123.0 4.0 m0 u6 MANDELLA You didn't 10 things i hate about you 1999
L721 123.0 5.0 m0 u5 KAT I got drunk. I puked. I got rejected. It was big fun. 10 things i hate about you 1999
L750 124.0 0.0 m0 u5 KAT Can you even imagine? Who the hell would go to this a bastion of commercial excess? 10 things i hate about you 1999
L751 124.0 1.0 m0 u6 MANDELLA Well, I guess we're not, since we don't have dates . 10 things i hate about you 1999
L752 124.0 2.0 m0 u5 KAT Listen to you! You sound like Betty, all pissed off because Archie is taking Veronica. 10 things i hate about you 1999
L753 124.0 3.0 m0 u6 MANDELLA Okay, okay, we won't go. It's not like I have a dress anyway 10 things i hate about you 1999
L754 124.0 4.0 m0 u5 KAT You ' re looking at this from the wrong perspective. We're making a statement. 10 things i hate about you 1999
L755 124.0 5.0 m0 u6 MANDELLA Oh, good. Something new and different for us. 10 things i hate about you 1999
L946 125.0 0.0 m0 u6 MANDELLA Have you seen him? 10 things i hate about you 1999
L947 125.0 1.0 m0 u5 KAT Who? 10 things i hate about you 1999
L948 125.0 2.0 m0 u6 MANDELLA William - he asked me to meet him here. 10 things i hate about you 1999
L949 125.0 3.0 m0 u5 KAT Oh, honey -- tell me we haven't' progressed to full-on hallucinations. 10 things i hate about you 1999
L304 126.0 0.0 m0 u9 PATRICK I mean Wo-man. How ya doin'? 10 things i hate about you 1999
L305 126.0 1.0 m0 u5 KAT Sweating like a pig, actually. And yourself? 10 things i hate about you 1999
L306 126.0 2.0 m0 u9 PATRICK There's a way to get a guy's attention. 10 things i hate about you 1999
L307 126.0 3.0 m0 u5 KAT My mission in life. 10 things i hate about you 1999
L309 127.0 0.0 m0 u9 PATRICK Pick you up Friday, then 10 things i hate about you 1999
L310 127.0 1.0 m0 u5 KAT Oh, right. Friday. 10 things i hate about you 1999
L311 128.0 0.0 m0 u9 PATRICK The night I take you to places you've never been before. And back. 10 things i hate about you 1999
L312 128.0 1.0 m0 u5 KAT Like where? The 7-Eleven on Burnside? Do you even know my name, screwboy? 10 things i hate about you 1999
L313 128.0 2.0 m0 u9 PATRICK I know a lot more than that 10 things i hate about you 1999
L322 129.0 0.0 m0 u9 PATRICK You hate me don't you? 10 things i hate about you 1999
L323 129.0 1.0 m0 u5 KAT I don't really think you warrant that strong an emotion. 10 things i hate about you 1999
L324 129.0 2.0 m0 u9 PATRICK Then say you'll spend Dollar Night at the track with me. 10 things i hate about you 1999
L325 129.0 3.0 m0 u5 KAT And why would I do that? 10 things i hate about you 1999
L326 129.0 4.0 m0 u9 PATRICK Come on -- the ponies, the flat beer, you with money in your eyes, me with my hand on your ass... 10 things i hate about you 1999
L327 129.0 5.0 m0 u5 KAT You -- covered in my vomit. 10 things i hate about you 1999
L328 129.0 6.0 m0 u9 PATRICK Seven-thirty? 10 things i hate about you 1999
L330 130.0 0.0 m0 u5 KAT Are you following me? 10 things i hate about you 1999
L331 130.0 1.0 m0 u9 PATRICK I was in the laundromat. I saw your car. Thought I'd say hi. 10 things i hate about you 1999
L332 130.0 2.0 m0 u5 KAT Hi 10 things i hate about you 1999
L333 131.0 0.0 m0 u9 PATRICK You're not a big talker, are you? 10 things i hate about you 1999
L334 131.0 1.0 m0 u5 KAT Depends on the topic. My fenders don't really whip me into a verbal frenzy. 10 things i hate about you 1999
L473 132.0 0.0 m0 u9 PATRICK hey. Great show, huh? 10 things i hate about you 1999
L474 132.0 1.0 m0 u5 KAT 10 things i hate about you 1999
L475 133.0 0.0 m0 u9 PATRICK Excuse me? 10 things i hate about you 1999
L476 133.0 1.0 m0 u5 KAT That's what you want, isn't it? 10 things i hate about you 1999
L477 133.0 2.0 m0 u9 PATRICK Do you mind? You're sort of ruining it for me. 10 things i hate about you 1999
L481 134.0 0.0 m0 u9 PATRICK You know, these guys are no Bikini Kill or The Raincoats, but they're right up there. 10 things i hate about you 1999
L482 134.0 1.0 m0 u5 KAT You know who The Raincoats are? 10 things i hate about you 1999
L483 134.0 2.0 m0 u9 PATRICK Why, don't you? 10 things i hate about you 1999
L564 135.0 0.0 m0 u9 PATRICK What's this? 10 things i hate about you 1999
L565 135.0 1.0 m0 u5 KAT "I'm getting trashed, man." Isn't that what you're supposed to do at a party? 10 things i hate about you 1999
L566 135.0 2.0 m0 u9 PATRICK I say, do what you wanna do. 10 things i hate about you 1999
L567 135.0 3.0 m0 u5 KAT Funny, you're the only one 10 things i hate about you 1999
L610 136.0 0.0 m0 u9 PATRICK Okay? 10 things i hate about you 1999
L611 136.0 1.0 m0 u5 KAT I'm fine. I'm 10 things i hate about you 1999
L612 137.0 0.0 m0 u9 PATRICK You're not okay. 10 things i hate about you 1999
L613 137.0 1.0 m0 u5 KAT I just need to lie down for awhile 10 things i hate about you 1999
L614 137.0 2.0 m0 u9 PATRICK Uh, uh. You lie down and you'll go to sleep 10 things i hate about you 1999
L615 137.0 3.0 m0 u5 KAT I know, just let me sleep 10 things i hate about you 1999
L616 137.0 4.0 m0 u9 PATRICK What if you have a concussion? My dog went to sleep with a concussion and woke up a vegetable. Not that I could tell the difference... 10 things i hate about you 1999
L626 138.0 0.0 m0 u5 KAT This is so patronizing. 10 things i hate about you 1999
L627 138.0 1.0 m0 u9 PATRICK Leave it to you to use big words when you're shitfaced. 10 things i hate about you 1999
L628 138.0 2.0 m0 u5 KAT Why 're you doing this? 10 things i hate about you 1999
L629 138.0 3.0 m0 u9 PATRICK I told you 10 things i hate about you 1999
L630 138.0 4.0 m0 u5 KAT You don't care if I die 10 things i hate about you 1999
L631 138.0 5.0 m0 u9 PATRICK Sure, I do 10 things i hate about you 1999
L632 138.0 6.0 m0 u5 KAT Why? 10 things i hate about you 1999
L633 138.0 7.0 m0 u9 PATRICK Because then I'd have to start taking out girls who like me. 10 things i hate about you 1999
L634 138.0 8.0 m0 u5 KAT Like you could find one 10 things i hate about you 1999
L635 138.0 9.0 m0 u9 PATRICK See that? Who needs affection when I've got blind hatred? 10 things i hate about you 1999
L636 138.0 10.0 m0 u5 KAT Just let me sit down. 10 things i hate about you 1999
L639 139.0 0.0 m0 u9 PATRICK Why'd you let him get to you? 10 things i hate about you 1999
L640 139.0 1.0 m0 u5 KAT Who? 10 things i hate about you 1999
L641 139.0 2.0 m0 u9 PATRICK Dorsey. 10 things i hate about you 1999
L642 139.0 3.0 m0 u5 KAT I hate him. 10 things i hate about you 1999
L643 139.0 4.0 m0 u9 PATRICK I know. It'd have to be a pretty big deal to get you to mainline tequila. You don't seem like the type. 10 things i hate about you 1999
L644 139.0 5.0 m0 u5 KAT Hey man. . . You don ' t think I can be "cool"? You don't think I can be "laid back" like everyone else? 10 things i hate about you 1999
L645 139.0 6.0 m0 u9 PATRICK I thought you were above all that 10 things i hate about you 1999
L646 139.0 7.0 m0 u5 KAT You know what they say 10 things i hate about you 1999
L650 140.0 0.0 m0 u9 PATRICK Kat! Wake up! 10 things i hate about you 1999
L651 140.0 1.0 m0 u5 KAT What? 10 things i hate about you 1999
L668 141.0 0.0 m0 u9 PATRICK And I'm in control of it. 10 things i hate about you 1999
L669 141.0 1.0 m0 u5 KAT But it's Gigglepuss - I know you like them. I saw you there. 10 things i hate about you 1999
L670 142.0 0.0 m0 u5 KAT When you were gone last year -- where were you? 10 things i hate about you 1999
L671 142.0 1.0 m0 u9 PATRICK Busy 10 things i hate about you 1999
L672 142.0 2.0 m0 u5 KAT Were you in jail? 10 things i hate about you 1999
L673 142.0 3.0 m0 u9 PATRICK Maybe. 10 things i hate about you 1999
L674 142.0 4.0 m0 u5 KAT No, you weren't 10 things i hate about you 1999
L675 142.0 5.0 m0 u9 PATRICK Then why'd you ask? 10 things i hate about you 1999
L676 142.0 6.0 m0 u5 KAT Why'd you lie? 10 things i hate about you 1999
L677 143.0 0.0 m0 u5 KAT I should do this. 10 things i hate about you 1999
L678 143.0 1.0 m0 u9 PATRICK Do what? 10 things i hate about you 1999
L679 143.0 2.0 m0 u5 KAT This. 10 things i hate about you 1999
L680 144.0 0.0 m0 u9 PATRICK Start a band? 10 things i hate about you 1999
L681 144.0 1.0 m0 u5 KAT My father wouldn't approve of that that 10 things i hate about you 1999
L682 144.0 2.0 m0 u9 PATRICK You don't strike me as the type that would ask permission. 10 things i hate about you 1999
L683 145.0 0.0 m0 u5 KAT Oh, so now you think you know me? 10 things i hate about you 1999
L684 145.0 1.0 m0 u9 PATRICK I'm gettin' there 10 things i hate about you 1999
L686 146.0 0.0 m0 u9 PATRICK So what ' s up with your dad? He a pain in the ass? 10 things i hate about you 1999
L687 146.0 1.0 m0 u5 KAT He just wants me to be someone I'm not. 10 things i hate about you 1999
L688 146.0 2.0 m0 u9 PATRICK Who? 10 things i hate about you 1999
L689 146.0 3.0 m0 u5 KAT BIANCA 10 things i hate about you 1999
L690 146.0 4.0 m0 u9 PATRICK No offense, but you're sister is without. I know everyone likes her and all, but ... 10 things i hate about you 1999
L764 147.0 0.0 m0 u9 PATRICK Excuse me, have you seen The Feminine Mystique? I lost my copy. 10 things i hate about you 1999
L765 147.0 1.0 m0 u5 KAT What are you doing here? 10 things i hate about you 1999
L766 147.0 2.0 m0 u9 PATRICK I heard there was a poetry reading. 10 things i hate about you 1999
L767 147.0 3.0 m0 u5 KAT You 're so -- 10 things i hate about you 1999
L768 147.0 4.0 m0 u9 PATRICK Pleasant? 10 things i hate about you 1999
L769 148.0 0.0 m0 u9 PATRICK Wholesome. 10 things i hate about you 1999
L770 148.0 1.0 m0 u5 KAT Unwelcome. 10 things i hate about you 1999
L771 148.0 2.0 m0 u9 PATRICK Unwelcome? I guess someone still has her panties in a twist. 10 things i hate about you 1999
L772 148.0 3.0 m0 u5 KAT Don't for one minute think that you had any effect whatsoever on my panties. 10 things i hate about you 1999
L773 148.0 4.0 m0 u9 PATRICK So what did I have an effect on ? 10 things i hate about you 1999
L774 148.0 5.0 m0 u5 KAT Other than my upchuck reflex? Nothing. 10 things i hate about you 1999
L798 149.0 0.0 m0 u5 KAT He left! I sprung the dickhead and he cruised on me. 10 things i hate about you 1999
L799 149.0 1.0 m0 u9 PATRICK Look up, sunshine 10 things i hate about you 1999
L800 150.0 0.0 m0 u9 PATRICK I guess I never told you I'm afraid of heights. 10 things i hate about you 1999
L801 150.0 1.0 m0 u5 KAT C'mon. It's not that bad 10 things i hate about you 1999
L802 150.0 2.0 m0 u9 PATRICK Try lookin' at it from this angle 10 things i hate about you 1999
L803 151.0 0.0 m0 u5 KAT Put your right foot there -- 10 things i hate about you 1999
L804 151.0 1.0 m0 u9 PATRICK Forget it. I'm stayin'. 10 things i hate about you 1999
L805 151.0 2.0 m0 u5 KAT You want me to climb up and show you how to get down? 10 things i hate about you 1999
L806 151.0 3.0 m0 u9 PATRICK Maybe. 10 things i hate about you 1999
L808 152.0 0.0 m0 u5 KAT The Partridge Family? 10 things i hate about you 1999
L809 152.0 1.0 m0 u9 PATRICK I figured it had to be something ridiculous to win your respect. And piss you off. 10 things i hate about you 1999
L810 152.0 2.0 m0 u5 KAT Good call. 10 things i hate about you 1999
L811 152.0 3.0 m0 u9 PATRICK So how'd you get Chapin to look the other way? 10 things i hate about you 1999
L812 152.0 4.0 m0 u5 KAT I dazzled him with my wit 10 things i hate about you 1999
L813 153.0 0.0 m0 u9 PATRICK A soft side? Who knew? 10 things i hate about you 1999
L814 153.0 1.0 m0 u5 KAT Yeah, well, don't let it get out 10 things i hate about you 1999
L815 153.0 2.0 m0 u9 PATRICK So what's your excuse? 10 things i hate about you 1999
L816 153.0 3.0 m0 u5 KAT Acting the way we do. 10 things i hate about you 1999
L817 153.0 4.0 m0 u9 PATRICK Yes 10 things i hate about you 1999
L818 153.0 5.0 m0 u5 KAT I don't like to do what people expect. Then they expect it all the time and they get disappointed when you change. 10 things i hate about you 1999
L819 153.0 6.0 m0 u9 PATRICK So if you disappoint them from the start, you're covered? 10 things i hate about you 1999
L820 153.0 7.0 m0 u5 KAT Something like that 10 things i hate about you 1999
L821 153.0 8.0 m0 u9 PATRICK Then you screwed up 10 things i hate about you 1999
L822 153.0 9.0 m0 u5 KAT How? 10 things i hate about you 1999
L823 153.0 10.0 m0 u9 PATRICK You never disappointed me. 10 things i hate about you 1999
L824 154.0 0.0 m0 u9 PATRICK You up for it? 10 things i hate about you 1999
L825 154.0 1.0 m0 u5 KAT For. . . ? 10 things i hate about you 1999
L828 155.0 0.0 m0 u5 KAT State trooper? 10 things i hate about you 1999
L829 155.0 1.0 m0 u9 PATRICK Fallacy. 10 things i hate about you 1999
L830 155.0 2.0 m0 u5 KAT The duck? 10 things i hate about you 1999
L831 155.0 3.0 m0 u9 PATRICK Hearsay. 10 things i hate about you 1999
L832 155.0 4.0 m0 u5 KAT I know the porn career's a lie. 10 things i hate about you 1999
L834 156.0 0.0 m0 u5 KAT Tell me something true. 10 things i hate about you 1999
L835 156.0 1.0 m0 u9 PATRICK I hate peas. 10 things i hate about you 1999
L836 156.0 2.0 m0 u5 KAT No -- something real. Something no one else knows. 10 things i hate about you 1999
L837 156.0 3.0 m0 u9 PATRICK You're sweet. And sexy. And completely hot for me. 10 things i hate about you 1999
L838 156.0 4.0 m0 u5 KAT What? 10 things i hate about you 1999
L839 156.0 5.0 m0 u9 PATRICK No one else knows 10 things i hate about you 1999
L840 156.0 6.0 m0 u5 KAT You're amazingly self-assured. Has anyone ever told you that? 10 things i hate about you 1999
L841 156.0 7.0 m0 u9 PATRICK Go to the prom with me 10 things i hate about you 1999
L842 157.0 0.0 m0 u5 KAT Is that a request or a command? 10 things i hate about you 1999
L843 157.0 1.0 m0 u9 PATRICK You know what I mean 10 things i hate about you 1999
L844 157.0 2.0 m0 u5 KAT No. 10 things i hate about you 1999
L845 157.0 3.0 m0 u9 PATRICK No what? 10 things i hate about you 1999
L846 157.0 4.0 m0 u5 KAT No, I won't go with you 10 things i hate about you 1999
L847 157.0 5.0 m0 u9 PATRICK Why not? 10 things i hate about you 1999
L848 157.0 6.0 m0 u5 KAT Because I don't want to. It's a stupid tradition. 10 things i hate about you 1999
L852 158.0 0.0 m0 u5 KAT Create a little drama? Start a new rumor? What? 10 things i hate about you 1999
L853 158.0 1.0 m0 u9 PATRICK So I have to have a motive to be with you? 10 things i hate about you 1999
L854 158.0 2.0 m0 u5 KAT You tell me. 10 things i hate about you 1999
L855 158.0 3.0 m0 u9 PATRICK You need therapy. Has anyone ever told you that? 10 things i hate about you 1999
L856 158.0 4.0 m0 u5 KAT Answer the question, Patrick 10 things i hate about you 1999
L857 158.0 5.0 m0 u9 PATRICK Nothing! There's nothing in it for me. Just the pleasure of your company. 10 things i hate about you 1999
L936 159.0 0.0 m0 u5 KAT How'd you get a tux at the last minute? 10 things i hate about you 1999
L937 159.0 1.0 m0 u9 PATRICK It's Scurvy's. His date got convicted. Where'd you get the dress? 10 things i hate about you 1999
L938 159.0 2.0 m0 u5 KAT It's just something I had. You know 10 things i hate about you 1999
L939 159.0 3.0 m0 u9 PATRICK Oh huh 10 things i hate about you 1999
L940 159.0 4.0 m0 u5 KAT Look, I'm -- sorry -- that I questioned your motives. I was wrong. 10 things i hate about you 1999
L957 160.0 0.0 m0 u9 PATRICK My grandmother's . 10 things i hate about you 1999
L958 160.0 1.0 m0 u5 KAT What? 10 things i hate about you 1999
L959 160.0 2.0 m0 u9 PATRICK That's where I was last year. She'd never lived alone -- my grandfather died -- I stayed with her. I wasn't in jail, I don't know Marilyn Manson, and I've never slept with a Spice Girl. I spent a year sitting next to my grandma on the couch watching Wheel of Fortune. End of story. 10 things i hate about you 1999
L960 161.0 0.0 m0 u5 KAT That ' s completely adorable! 10 things i hate about you 1999
L961 161.0 1.0 m0 u9 PATRICK It gets worse -- you still have your freshman yearbook? 10 things i hate about you 1999
L973 162.0 0.0 m0 u9 PATRICK Wait I... 10 things i hate about you 1999
L974 162.0 1.0 m0 u5 KAT You were paid to take me out! By -- the one person I truly hate. I knew it was a set-up! 10 things i hate about you 1999
L975 162.0 2.0 m0 u9 PATRICK It wasn't like that. 10 things i hate about you 1999
L976 162.0 3.0 m0 u5 KAT Really? What was it like? A down payment now, then a bonus for sleeping with me? 10 things i hate about you 1999
L977 162.0 4.0 m0 u9 PATRICK I didn't care about the money. 10 things i hate about you 1999
L1031 163.0 0.0 m0 u5 KAT A Fender Strat. You bought this? 10 things i hate about you 1999
L1032 163.0 1.0 m0 u9 PATRICK I thought you could use it. When you start your band. 10 things i hate about you 1999
L1033 164.0 0.0 m0 u9 PATRICK Besides, I had some extra cash. Some asshole paid me to take out a really great girl. 10 things i hate about you 1999
L1034 164.0 1.0 m0 u5 KAT Is that right? 10 things i hate about you 1999
L1035 164.0 2.0 m0 u9 PATRICK Yeah, but then I fucked up. I fell for her. 10 things i hate about you 1999
L1040 165.0 0.0 m0 u5 KAT Why is my veggie burger the only burnt object on this grill? 10 things i hate about you 1999
L1041 165.0 1.0 m0 u9 PATRICK Because I like to torture you. 10 things i hate about you 1999
L1042 165.0 2.0 m0 u5 KAT Oh, Bianca? Can you get me my freshman yearbook? 10 things i hate about you 1999
L1043 165.0 3.0 m0 u9 PATRICK Don ' t you even dare. . . 10 things i hate about you 1999
L172 166.0 0.0 m0 u5 KAT I know. 10 things i hate about you 1999
L173 166.0 1.0 m0 u11 WALTER I thought we decided you were going to school here. At U of 0. 10 things i hate about you 1999
L174 166.0 2.0 m0 u5 KAT You decided. 10 things i hate about you 1999
L184 167.0 0.0 m0 u5 KAT This from someone whose diary is devoted to favorite grooming tips? 10 things i hate about you 1999
L185 167.0 1.0 m0 u11 WALTER Enough! 10 things i hate about you 1999
L338 168.0 0.0 m0 u11 WALTER My insurance does not cover PMS 10 things i hate about you 1999
L339 168.0 1.0 m0 u5 KAT Then tell them I had a seizure. 10 things i hate about you 1999
L340 168.0 2.0 m0 u11 WALTER Is this about Sarah Lawrence? You punishing me? 10 things i hate about you 1999
L341 168.0 3.0 m0 u5 KAT I thought you were punishing me. 10 things i hate about you 1999
L342 168.0 4.0 m0 u11 WALTER Why can't we agree on this? 10 things i hate about you 1999
L343 168.0 5.0 m0 u5 KAT Because you're making decisions for me. 10 things i hate about you 1999
L344 168.0 6.0 m0 u11 WALTER As a parent, that's my right 10 things i hate about you 1999
L345 168.0 7.0 m0 u5 KAT So what I want doesn't matter? 10 things i hate about you 1999
L346 168.0 8.0 m0 u11 WALTER You're eighteen. You don't know what you want. You won't know until you're forty-five and you don't have it. 10 things i hate about you 1999
L347 168.0 9.0 m0 u5 KAT I want to go to an East Coast school! I want you to trust me to make my own choices. I want -- 10 things i hate about you 1999
L986 169.0 0.0 m0 u11 WALTER Was that your sister? 10 things i hate about you 1999
L987 169.0 1.0 m0 u5 KAT Yeah. She left with some bikers Big ones. Full of sperm. 10 things i hate about you 1999
L988 169.0 2.0 m0 u11 WALTER Funny. 10 things i hate about you 1999
L989 170.0 0.0 m0 u11 WALTER I don't understand the allure of dehydrated food. Is this something I should be hip to? 10 things i hate about you 1999
L990 170.0 1.0 m0 u5 KAT No, Daddy. 10 things i hate about you 1999
L991 170.0 2.0 m0 u11 WALTER So tell me about this dance. Was it fun? 10 things i hate about you 1999
L992 170.0 3.0 m0 u5 KAT Parts of it. 10 things i hate about you 1999
L993 170.0 4.0 m0 u11 WALTER Which parts? 10 things i hate about you 1999
L994 170.0 5.0 m0 u5 KAT The part where Bianca beat the hell out of some guy. 10 things i hate about you 1999
L995 170.0 6.0 m0 u11 WALTER Bianca did what? 10 things i hate about you 1999
L996 170.0 7.0 m0 u5 KAT What's the matter? Upset that I rubbed off on her? 10 things i hate about you 1999
L997 170.0 8.0 m0 u11 WALTER No -- impressed. 10 things i hate about you 1999
L998 171.0 0.0 m0 u11 WALTER You know, fathers don't like to admit that their daughters are capable of running their own lives. It means we've become spectators. Bianca still lets me play a few innings. You've had me on the bleachers for years. When you go to Sarah Lawrence, I won't even be able to watch the game. 10 things i hate about you 1999
L999 171.0 1.0 m0 u5 KAT When I go? 10 things i hate about you 1999
L1000 171.0 2.0 m0 u11 WALTER Oh, Christ. Don't tell me you've changed your mind. I already sent 'em a check. 10 things i hate about you 1999
L103 172.0 0.0 m0 u8 MISS PERKY Katarina Stratford. My, my. You've been terrorizing Ms. Blaise again. 10 things i hate about you 1999
L104 172.0 1.0 m0 u5 KAT Expressing my opinion is not a terrorist action. 10 things i hate about you 1999
L105 172.0 2.0 m0 u8 MISS PERKY Well, yes, compared to your other choices of expression this year, today's events are quite mild. By the way, Bobby Rictor's gonad retrieval operation went quite well, in case you're interested. 10 things i hate about you 1999
L106 172.0 3.0 m0 u5 KAT I still maintain that he kicked himself in the balls. I was merely a spectator. 10 things i hate about you 1999
L107 172.0 4.0 m0 u8 MISS PERKY The point is Kat -- people perceive you as somewhat ... 10 things i hate about you 1999
L108 173.0 0.0 m0 u5 KAT Tempestuous? 10 things i hate about you 1999
L109 173.0 1.0 m0 u8 MISS PERKY No ... I believe "heinous bitch" is the term used most often. 10 things i hate about you 1999
L1017 174.0 0.0 m0 u5 KAT Am I supposed to feel better? Like, right now? Or do I have some time to think about it? 10 things i hate about you 1999
L1018 174.0 1.0 m0 u8 MISS PERKY Just smack her now. 10 things i hate about you 1999
L728 175.0 0.0 m0 u7 MICHAEL Hey there. Tired of breathing? 10 things i hate about you 1999
L729 175.0 1.0 m0 u6 MANDELLA Hi. 10 things i hate about you 1999
L730 175.0 2.0 m0 u7 MICHAEL Cool pictures. You a fan? 10 things i hate about you 1999
L731 175.0 3.0 m0 u6 MANDELLA Yeah. I guess. 10 things i hate about you 1999
L732 176.0 0.0 m0 u6 MANDELLA You think? 10 things i hate about you 1999
L733 176.0 1.0 m0 u7 MICHAEL Oh yeah. 10 things i hate about you 1999
L735 177.0 0.0 m0 u7 MICHAEL Macbeth, right? 10 things i hate about you 1999
L736 177.0 1.0 m0 u6 MANDELLA Right. 10 things i hate about you 1999
L737 177.0 2.0 m0 u7 MICHAEL Kat a fan, too? 10 things i hate about you 1999
L738 177.0 3.0 m0 u6 MANDELLA Yeah... 10 things i hate about you 1999
L371 178.0 0.0 m0 u9 PATRICK Say it 10 things i hate about you 1999
L372 178.0 1.0 m0 u7 MICHAEL What? 10 things i hate about you 1999
L373 178.0 2.0 m0 u9 PATRICK Whatever the hell it is you're standin' there waitin' to say. 10 things i hate about you 1999
L375 179.0 0.0 m0 u9 PATRICK What plan? 10 things i hate about you 1999
L376 179.0 1.0 m0 u7 MICHAEL The situation is, my man Cameron here has a major jones for Bianca Stratford. 10 things i hate about you 1999
L377 179.0 2.0 m0 u9 PATRICK What is it with this chick? She have three tits? 10 things i hate about you 1999
L378 180.0 0.0 m0 u7 MICHAEL I think I speak correctly when I say that Cameron's love is pure. Purer than say -- Joey Dorsey's. 10 things i hate about you 1999
L379 180.0 1.0 m0 u9 PATRICK Dorsey can plow whoever he wants. I'm just in this for the cash. 10 things i hate about you 1999
L380 181.0 0.0 m0 u7 MICHAEL That's where we can help you. With Kat. 10 things i hate about you 1999
L381 181.0 1.0 m0 u9 PATRICK So Dorsey can get the girl? 10 things i hate about you 1999
L382 181.0 2.0 m0 u7 MICHAEL Patrick, Pat, you're not looking at the big picture. Joey's just a pawn. We set this whole thing up so Cameron can get the girl. 10 things i hate about you 1999
L383 182.0 0.0 m0 u9 PATRICK You two are gonna help me tame the wild beast? 10 things i hate about you 1999
L384 182.0 1.0 m0 u7 MICHAEL We're your guys. 10 things i hate about you 1999
L414 183.0 0.0 m0 u9 PATRICK What?! 10 things i hate about you 1999
L415 183.0 1.0 m0 u7 MICHAEL Good enough. 10 things i hate about you 1999
L420 184.0 0.0 m0 u9 PATRICK Are you telling me I'm a - "non-smoker"? 10 things i hate about you 1999
L421 184.0 1.0 m0 u7 MICHAEL Just for now. 10 things i hate about you 1999
L428 185.0 0.0 m0 u7 MICHAEL Ever been to Club Skunk? 10 things i hate about you 1999
L429 185.0 1.0 m0 u9 PATRICK Yeah. 10 things i hate about you 1999
L436 186.0 0.0 m0 u7 MICHAEL I prefer to think of it simply as an alternative to what the law allows. 10 things i hate about you 1999
L437 186.0 1.0 m0 u9 PATRICK I'm likin' you guys better 10 things i hate about you 1999
L723 187.0 0.0 m0 u7 MICHAEL So you got cozy with she who stings? 10 things i hate about you 1999
L724 187.0 1.0 m0 u9 PATRICK No - I've got a sweet-payin' job that I'm about to lose. 10 things i hate about you 1999
L775 188.0 0.0 m0 u9 PATRICK You were right. She's still pissed. 10 things i hate about you 1999
L776 188.0 1.0 m0 u7 MICHAEL Sweet love, renew thy force! 10 things i hate about you 1999
L777 188.0 2.0 m0 u9 PATRICK Man -- don't say shit like that to me. People can hear you. 10 things i hate about you 1999
L59 189.0 0.0 m0 u9 PATRICK I missed you. 10 things i hate about you 1999
L60 189.0 1.0 m0 u8 MISS PERKY It says here you exposed yourself to a group of freshmen girls. 10 things i hate about you 1999
L61 189.0 2.0 m0 u9 PATRICK It was a bratwurst. I was eating lunch. 10 things i hate about you 1999
L62 189.0 3.0 m0 u8 MISS PERKY With the teeth of your zipper? 10 things i hate about you 1999
L257 190.0 0.0 m0 u8 MISS PERKY I don't understand, Patrick. You haven't done anything asinine this week. Are you not feeling well? 10 things i hate about you 1999
L258 190.0 1.0 m0 u9 PATRICK Touch of the flu. 10 things i hate about you 1999
L259 190.0 2.0 m0 u8 MISS PERKY I'm at a loss, then. What should we talk about? Your year of absence? 10 things i hate about you 1999
L261 191.0 0.0 m0 u8 MISS PERKY Why don't we discuss your driving need to be a hemorrhoid? 10 things i hate about you 1999
L262 191.0 1.0 m0 u9 PATRICK What's to discuss? 10 things i hate about you 1999
L263 191.0 2.0 m0 u8 MISS PERKY You weren't abused, you aren't stupid, and as far as I can tell, you're only slightly psychotic -- so why is it that you're such a fuck-up? 10 things i hate about you 1999
L264 191.0 3.0 m0 u9 PATRICK Well, you know -- there's the prestige of the job title... and the benefits package is pretty good... 10 things i hate about you 1999
L511 192.0 0.0 m0 u8 MISS PERKY You're completely demented. 10 things i hate about you 1999
L512 192.0 1.0 m0 u9 PATRICK See you next week! 10 things i hate about you 1999
L161 193.0 0.0 m0 u10 SHARON In the microwave. 10 things i hate about you 1999
L162 193.0 1.0 m0 u11 WALTER Make anyone cry today? 10 things i hate about you 1999
L170 194.0 0.0 m0 u10 SHARON What's a synonym for throbbing? 10 things i hate about you 1999
L171 194.0 1.0 m0 u11 WALTER Sarah Lawrence is on the other side of the country. 10 things i hate about you 1999
L191 195.0 0.0 m0 u11 WALTER Jesus! Can a man even grab a sandwich before you women start dilating? 10 things i hate about you 1999
L192 195.0 1.0 m0 u10 SHARON Tumescent! 10 things i hate about you 1999
L193 195.0 2.0 m0 u11 WALTER You're not helping. 10 things i hate about you 1999
L876 196.0 0.0 m0 u10 SHARON Would you rather be ravished by a pirate or a British rear admiral? 10 things i hate about you 1999
L877 196.0 1.0 m0 u11 WALTER Pirate -- no question. 10 things i hate about you 1999
L886 197.0 0.0 m0 u10 SHARON They'll dance, they'll kiss, they'll come home. Let her go. 10 things i hate about you 1999
L887 197.0 1.0 m0 u11 WALTER Kissing? Is that what you think happens? Kissing isn't what keeps me up to my elbows in placenta all day. 10 things i hate about you 1999
L917 198.0 0.0 m0 u11 WALTER What do you wanna watch? We've got crap, crap, crap or crap 10 things i hate about you 1999
L918 198.0 1.0 m0 u10 SHARON Dr. Ruth? 10 things i hate about you 1999
L926 199.0 0.0 m0 u10 SHARON Have a great time, honey! 10 things i hate about you 1999
L927 199.0 1.0 m0 u11 WALTER But -- who -- what --? 10 things i hate about you 1999
L929 200.0 0.0 m0 u11 WALTER What just happened? 10 things i hate about you 1999
L930 200.0 1.0 m0 u10 SHARON Your daughters went to the prom. 10 things i hate about you 1999
L931 200.0 2.0 m0 u11 WALTER Did I have anything to say about it? 10 things i hate about you 1999
L932 200.0 3.0 m0 u10 SHARON Absolutely not. 10 things i hate about you 1999
L933 200.0 4.0 m0 u11 WALTER That ' s what I thought 10 things i hate about you 1999
L2170 201.0 0.0 m1 u12 ALONSO I never seen heat like this! Not even in Las Minas! 1492: conquest of paradise 1992
L2171 201.0 1.0 m1 u23 SAILOR The water's going putrid in the barrels. 1492: conquest of paradise 1992
L2172 201.0 2.0 m1 u12 ALONSO You'll be drinking your own piss... For the glory of Spain... and Admiral Colon...! Bastard! 1492: conquest of paradise 1992
L2173 202.0 0.0 m1 u12 ALONSO What are you listening to, chicken ass? 1492: conquest of paradise 1992
L2174 202.0 1.0 m1 u23 SAILOR Ah, leave him alone. He's doing no harm. 1492: conquest of paradise 1992
L2175 202.0 2.0 m1 u12 ALONSO With a face like that? I don't want you looking at me. You hear? 1492: conquest of paradise 1992
L2176 203.0 0.0 m1 u12 ALONSO He's the devil's child... 1492: conquest of paradise 1992
L2177 203.0 1.0 m1 u23 SAILOR We'll all go crazy... 1492: conquest of paradise 1992
L2179 204.0 0.0 m1 u12 ALONSO We should have seen land. 1492: conquest of paradise 1992
L2180 204.0 1.0 m1 u23 SAILOR We left three weeks ago, Alonso. Can't be that near. 1492: conquest of paradise 1992
L2181 204.0 2.0 m1 u12 ALONSO Can't be that far, I say. Also, I don't like the smell of the sea around here. Smells like a cunt. Bad sign... 1492: conquest of paradise 1992
L1989 205.0 0.0 m1 u13 AROJAZ You say Asia can be found by sailing west? 1492: conquest of paradise 1992
L1990 205.0 1.0 m1 u16 COLUMBUS Yes, your Eminence. The voyage should not take more than six or seven weeks. 1492: conquest of paradise 1992
L1991 205.0 2.0 m1 u13 AROJAZ Unfortunately, Don Colon, that is precisely where our opinions differ... Are you familiar with the work of Aristotle? Erathostene? Ptolemeus? 1492: conquest of paradise 1992
L1992 205.0 3.0 m1 u16 COLUMBUS I am, Your Eminence 1492: conquest of paradise 1992
L1993 205.0 4.0 m1 u13 AROJAZ Then you cannot ignore that according to their calculations, the circumference of the Earth is approximately... 22,000 leagues or more. Which makes the ocean... uncrossable. 1492: conquest of paradise 1992
L2005 206.0 0.0 m1 u13 AROJAZ Senor Colon, an experienced captain such as yourself will understand our concern with the crew. I am not willing to have on my conscience the loss of men who would have relied upon our judgment. 1492: conquest of paradise 1992
L2006 206.0 1.0 m1 u16 COLUMBUS Excellency, you are right. 1492: conquest of paradise 1992
L2010 207.0 0.0 m1 u16 COLUMBUS Your Eminence, there is only one way to settle the matter. And that is to make the journey. I am ready to risk my life to prove it possible. 1492: conquest of paradise 1992
L2011 207.0 1.0 m1 u13 AROJAZ Your life, and that of others! 1492: conquest of paradise 1992
L2012 207.0 2.0 m1 u16 COLUMBUS If they agree to follow me, yes. 1492: conquest of paradise 1992
L2014 208.0 0.0 m1 u16 COLUMBUS Trade, Your Excellency. According to Marco Polo, the Kingdom of China is one of the richest of the world. Even the meanest buildings are roofed with gold. 1492: conquest of paradise 1992
L2015 208.0 1.0 m1 u13 AROJAZ Is that all that interests you? Gold? 1492: conquest of paradise 1992
L2016 208.0 2.0 m1 u16 COLUMBUS No. The Portuguese have already discovered black-skinned people. I, too, will find other populations -- and bring them to the word of God. 1492: conquest of paradise 1992
L2019 209.0 0.0 m1 u13 AROJAZ If God intended our proximity to Asia, do you believe he would have waited for you to show it to the world? 1492: conquest of paradise 1992
L2020 209.0 1.0 m1 u16 COLUMBUS Did He not choose a carpenter's son to reveal Himself to the world? 1492: conquest of paradise 1992
L2022 210.0 0.0 m1 u13 AROJAZ Don't you realize your words could be considered heretical? 1492: conquest of paradise 1992
L2023 210.0 1.0 m1 u16 COLUMBUS Blind faith is what I consider heresy! 1492: conquest of paradise 1992
L2024 211.0 0.0 m1 u16 COLUMBUS Asia can be found to the west -- and I will prove it. 1492: conquest of paradise 1992
L2025 211.0 1.0 m1 u13 AROJAZ IF-GOD-WILLS-IT! 1492: conquest of paradise 1992
L2027 212.0 0.0 m1 u24 SANCHEZ The State has some reason to be interested in this man's proposition, Your Eminence... 1492: conquest of paradise 1992
L2028 212.0 1.0 m1 u13 AROJAZ The Judgment is ours! 1492: conquest of paradise 1992
L2029 212.0 2.0 m1 u24 SANCHEZ Naturally. But I would really deplore the loss of such a potential opportunity for Spain for a... dispute over a point of geography. 1492: conquest of paradise 1992
L2030 213.0 0.0 m1 u13 AROJAZ He is a mercenary! Did he not already try to convince the King of Portugal of his absurd notions? 1492: conquest of paradise 1992
L2031 213.0 1.0 m1 u24 SANCHEZ Indeed. The world is full of mercenaries -- and states often make use of them, when it benefits them. My only concern is the welfare and prosperity of Spain. 1492: conquest of paradise 1992
L2274 214.0 0.0 m1 u13 AROJAZ It won't be easy to get rid of your prophet now, Don Sanchez. 1492: conquest of paradise 1992
L2275 214.0 1.0 m1 u24 SANCHEZ On the contrary, Your Eminence. It seems to me the man is preparing his own cross. 1492: conquest of paradise 1992
L2522 215.0 0.0 m1 u24 SANCHEZ You can see for yourself. 1492: conquest of paradise 1992
L2523 215.0 1.0 m1 u13 AROJAZ What a tragedy... what a waste of a life... 1492: conquest of paradise 1992
L2524 215.0 2.0 m1 u24 SANCHEZ A waste...? Let me tell you something, Arojaz. If your name, or mine, is ever remembered -- it will only be because of his. 1492: conquest of paradise 1992
L1979 216.0 0.0 m1 u16 COLUMBUS I could be gone for years. 1492: conquest of paradise 1992
L1980 216.0 1.0 m1 u14 BEATRIX I know. 1492: conquest of paradise 1992
L1981 216.0 2.0 m1 u16 COLUMBUS I haven't given you much of a life. 1492: conquest of paradise 1992
L1982 216.0 3.0 m1 u14 BEATRIX Well... that's true. I have a child by a man who won't marry me! Who's always leaving... 1492: conquest of paradise 1992
L1983 216.0 4.0 m1 u16 COLUMBUS Are we going to argue? 1492: conquest of paradise 1992
L1984 216.0 5.0 m1 u14 BEATRIX I'd love to argue with you sometimes. But you're never here! 1492: conquest of paradise 1992
L1985 217.0 0.0 m1 u16 COLUMBUS Perhaps I was never meant to live with a woman... 1492: conquest of paradise 1992
L1986 217.0 1.0 m1 u14 BEATRIX I find that hard to believe. 1492: conquest of paradise 1992
L2118 218.0 0.0 m1 u16 COLUMBUS She said yes. 1492: conquest of paradise 1992
L2119 218.0 1.0 m1 u14 BEATRIX Thank God... 1492: conquest of paradise 1992
L2121 219.0 0.0 m1 u14 BEATRIX I'm not asking you to swear to anything. 1492: conquest of paradise 1992
L2122 219.0 1.0 m1 u16 COLUMBUS I don't want you to wait for me. 1492: conquest of paradise 1992
L2123 219.0 2.0 m1 u14 BEATRIX That's something you can't decide. 1492: conquest of paradise 1992
L2318 220.0 0.0 m1 u16 COLUMBUS Beatrix, I want to ask you something. 1492: conquest of paradise 1992
L2319 220.0 1.0 m1 u14 BEATRIX You don't usually ask. 1492: conquest of paradise 1992
L2320 220.0 2.0 m1 u16 COLUMBUS I can arrange for the Queen to take Fernando and Diego into her service. 1492: conquest of paradise 1992
L2471 221.0 0.0 m1 u16 COLUMBUS God... you're so beautiful! I can't believe no other man has ever taken you away from me... 1492: conquest of paradise 1992
L2472 221.0 1.0 m1 u14 BEATRIX They tried... but I didn't let them. 1492: conquest of paradise 1992
L2473 222.0 0.0 m1 u14 BEATRIX They took everything... 1492: conquest of paradise 1992
L2474 222.0 1.0 m1 u16 COLUMBUS Not everything... Do you think I care? I'm a free man again. Riches don't make a man rich, they only make him busier... 1492: conquest of paradise 1992
L2537 223.0 0.0 m1 u16 COLUMBUS Can't you stay with us a little? 1492: conquest of paradise 1992
L2538 223.0 1.0 m1 u14 BEATRIX I am busy inside. 1492: conquest of paradise 1992
L2539 224.0 0.0 m1 u14 BEATRIX What is it, now? Tell me... 1492: conquest of paradise 1992
L2540 224.0 1.0 m1 u16 COLUMBUS I can't keep my eyes off you. I would like to catch up with all the moments I didn't spend with you. 1492: conquest of paradise 1992
L2290 225.0 0.0 m1 u15 BOBADILLA I understand that you will soon be appointing Governors for the islands? Is it not so? 1492: conquest of paradise 1992
L2291 225.0 1.0 m1 u16 COLUMBUS Forgive me, Don Bobadilla -- those positions have already been taken. 1492: conquest of paradise 1992
L2292 225.0 2.0 m1 u15 BOBADILLA May I ask by whom? 1492: conquest of paradise 1992
L2293 225.0 3.0 m1 u16 COLUMBUS Bartolome and Giacomo Colon. 1492: conquest of paradise 1992
L2421 226.0 0.0 m1 u15 BOBADILLA Don Alonso de Bobadilla. 1492: conquest of paradise 1992
L2422 226.0 1.0 m1 u16 COLUMBUS Yes... I remember... 1492: conquest of paradise 1992
L2423 227.0 0.0 m1 u15 BOBADILLA My letters of appointment. 1492: conquest of paradise 1992
L2424 227.0 1.0 m1 u16 COLUMBUS Appointment to what? 1492: conquest of paradise 1992
L2425 227.0 2.0 m1 u15 BOBADILLA Viceroy of the West Indies. 1492: conquest of paradise 1992
L2426 227.0 3.0 m1 u16 COLUMBUS Congratulations. Then I am free to search for the mainland. 1492: conquest of paradise 1992
L2429 228.0 0.0 m1 u16 COLUMBUS How far from here? 1492: conquest of paradise 1992
L2430 228.0 1.0 m1 u15 BOBADILLA I am not a seaman. But I heard it is no more than a week at sea. I hope you are not too disappointed. 1492: conquest of paradise 1992
L2431 228.0 2.0 m1 u16 COLUMBUS How could I be? The mainland has been found. Exactly as I said it would. 1492: conquest of paradise 1992
L2432 228.0 3.0 m1 u15 BOBADILLA I am afraid this is not the worst news. 1492: conquest of paradise 1992
L2127 229.0 0.0 m1 u17 FERNANDO I want to go with you! 1492: conquest of paradise 1992
L2128 229.0 1.0 m1 u16 COLUMBUS There'll be a time. 1492: conquest of paradise 1992
L2129 229.0 2.0 m1 u17 FERNANDO You promise? Do you swear on St. Christopher...? 1492: conquest of paradise 1992
L2130 230.0 0.0 m1 u17 FERNANDO Do you swear on all the Holy Saints in heaven? 1492: conquest of paradise 1992
L2131 230.0 1.0 m1 u16 COLUMBUS Yes... Yes, I do... On all of them! 1492: conquest of paradise 1992
L2439 231.0 0.0 m1 u16 COLUMBUS I have to explore the mainland. 1492: conquest of paradise 1992
L2440 231.0 1.0 m1 u17 FERNANDO This time with me! 1492: conquest of paradise 1992
L2484 232.0 0.0 m1 u16 COLUMBUS How are you feeling, Fernando? 1492: conquest of paradise 1992
L2485 232.0 1.0 m1 u17 FERNANDO Not bad. 1492: conquest of paradise 1992
L2500 233.0 0.0 m1 u17 FERNANDO Father... 1492: conquest of paradise 1992
L2501 233.0 1.0 m1 u16 COLUMBUS There must be a passage to that other ocean. 1492: conquest of paradise 1992
L2541 234.0 0.0 m1 u16 COLUMBUS What are you listening to? 1492: conquest of paradise 1992
L2542 234.0 1.0 m1 u17 FERNANDO I am not listening, Father. But I can't help hearing. 1492: conquest of paradise 1992
L2547 235.0 0.0 m1 u16 COLUMBUS What does he say? 1492: conquest of paradise 1992
L2548 235.0 1.0 m1 u17 FERNANDO He asks when he can come to visit you. He left his address. 1492: conquest of paradise 1992
L2549 235.0 2.0 m1 u16 COLUMBUS He never had one... except aboard my ships! 1492: conquest of paradise 1992
L2550 236.0 0.0 m1 u17 FERNANDO I want you to tell me everything you remember, Father. From the beginning. Everything. 1492: conquest of paradise 1992
L2551 236.0 1.0 m1 u16 COLUMBUS Really? God... I wouldn't know where to start... and yet... 1492: conquest of paradise 1992
L2552 236.0 2.0 m1 u17 FERNANDO Tell me the first thing that comes to your mind. 1492: conquest of paradise 1992
L2103 237.0 0.0 m1 u16 COLUMBUS No... 1492: conquest of paradise 1992
L2104 237.0 1.0 m1 u24 SANCHEZ No? 1492: conquest of paradise 1992
L2105 237.0 2.0 m1 u16 COLUMBUS NO...! I have waited too long, fought too hard. Now you expect me to take all the risks while you take the profit! No... I will not be your servant! 1492: conquest of paradise 1992
L2106 238.0 0.0 m1 u24 SANCHEZ I remind you, Senor Colon, that you are in no position to bargain with me. 1492: conquest of paradise 1992
L2107 238.0 1.0 m1 u16 COLUMBUS I'm not bargaining! 1492: conquest of paradise 1992
L2108 238.0 2.0 m1 u24 SANCHEZ Then you are too ambitious. 1492: conquest of paradise 1992
L2109 239.0 0.0 m1 u16 COLUMBUS And were you never ambitious, Excellency? Or is ambition only a virtue among the nobles, a fault for the rest of us? 1492: conquest of paradise 1992
L2110 239.0 1.0 m1 u24 SANCHEZ If you won't accept our proposal, we'll simply find someone who will. 1492: conquest of paradise 1992
L2281 240.0 0.0 m1 u16 COLUMBUS They don't see sin in their nakedness. They live according to nature, in a never ending summer. The islands are covered with trees, filled with blossoms and fruits. And... 1492: conquest of paradise 1992
L2282 240.0 1.0 m1 u24 SANCHEZ Forgive me, Don Colon. But what about gold? 1492: conquest of paradise 1992
L2286 241.0 0.0 m1 u24 SANCHEZ You defend yourself admirably... 1492: conquest of paradise 1992
L2287 241.0 1.0 m1 u16 COLUMBUS ... for a commoner? 1492: conquest of paradise 1992
L2295 242.0 0.0 m1 u16 COLUMBUS But we do have a lack of notaries. You should contact my administration. 1492: conquest of paradise 1992
L2296 242.0 1.0 m1 u24 SANCHEZ Don Bobadilla is already a judge, my Dear Don Cristobal. 1492: conquest of paradise 1992
L2297 242.0 2.0 m1 u16 COLUMBUS Good! We are also in need of judges. Except there are no thieves! 1492: conquest of paradise 1992
L2299 243.0 0.0 m1 u24 SANCHEZ You seem to have a special talent for making friends. 1492: conquest of paradise 1992
L2300 243.0 1.0 m1 u16 COLUMBUS What...? Do I have so many already? 1492: conquest of paradise 1992
L2301 243.0 2.0 m1 u24 SANCHEZ To rise so high, in so short a time, is a dangerous occupation. A little hypocrisy goes a long way. 1492: conquest of paradise 1992
L2463 244.0 0.0 m1 u24 SANCHEZ All I have to do is call the guards. 1492: conquest of paradise 1992
L2464 244.0 1.0 m1 u16 COLUMBUS Call them. 1492: conquest of paradise 1992
L2465 245.0 0.0 m1 u24 SANCHEZ I am not afraid of you. You are nothing but a dreamer. 1492: conquest of paradise 1992
L2466 245.0 1.0 m1 u16 COLUMBUS Look out of that window. 1492: conquest of paradise 1992
L2467 246.0 0.0 m1 u16 COLUMBUS What do you see? 1492: conquest of paradise 1992
L2468 246.0 1.0 m1 u24 SANCHEZ Roofs... towers, palaces... spires... 1492: conquest of paradise 1992
L2469 246.0 2.0 m1 u16 COLUMBUS All of them created by people like me. 1492: conquest of paradise 1992
L2237 247.0 0.0 m1 u25 UTAPAN Say not here! Cuba! 1492: conquest of paradise 1992
L2238 247.0 1.0 m1 u16 COLUMBUS What is it? A tribe? An island? 1492: conquest of paradise 1992
L2239 247.0 2.0 m1 u25 UTAPAN Island. Far. 1492: conquest of paradise 1992
L2248 248.0 0.0 m1 u25 UTAPAN You come! You speak first! 1492: conquest of paradise 1992
L2249 248.0 1.0 m1 u16 COLUMBUS Tell the Chief we thank him. 1492: conquest of paradise 1992
L2250 248.0 2.0 m1 u25 UTAPAN Chief knows. 1492: conquest of paradise 1992
L2251 248.0 3.0 m1 u16 COLUMBUS Tell him his country is very beautiful. Tell him we are leaving men here -- to build a fort. 1492: conquest of paradise 1992
L2253 249.0 0.0 m1 u25 UTAPAN Chief says -- how many? 1492: conquest of paradise 1992
L2254 249.0 1.0 m1 u16 COLUMBUS Thousands. 1492: conquest of paradise 1992
L2255 249.0 2.0 m1 u25 UTAPAN Why? 1492: conquest of paradise 1992
L2256 250.0 0.0 m1 u16 COLUMBUS To bring the word of God. 1492: conquest of paradise 1992
L2257 250.0 1.0 m1 u25 UTAPAN Chief says -- he has a God. 1492: conquest of paradise 1992
L2258 250.0 2.0 m1 u16 COLUMBUS ... and also to bring medicine. 1492: conquest of paradise 1992
L2259 250.0 3.0 m1 u25 UTAPAN Chief says... 1492: conquest of paradise 1992
L2260 250.0 4.0 m1 u16 COLUMBUS He has medicine. Tell him we admire his people. 1492: conquest of paradise 1992
L2341 251.0 0.0 m1 u16 COLUMBUS We will work with his people. We want peace. Ask the Chief if he understands? 1492: conquest of paradise 1992
L2342 251.0 1.0 m1 u25 UTAPAN He understands. 1492: conquest of paradise 1992
L2343 251.0 2.0 m1 u16 COLUMBUS Ask him if he will help. 1492: conquest of paradise 1992
L2393 252.0 0.0 m1 u16 COLUMBUS You have to find them, Utapan. Look what they did! 1492: conquest of paradise 1992
L2394 252.0 1.0 m1 u25 UTAPAN You did the same to your God! 1492: conquest of paradise 1992
L2409 253.0 0.0 m1 u16 COLUMBUS Utapan, won't you speak to me? You used to know how to speak to me. 1492: conquest of paradise 1992
L2410 253.0 1.0 m1 u25 UTAPAN You never learned how to speak my language. 1492: conquest of paradise 1992
L1942 254.0 0.0 m1 u19 MARCHENA Diego is a bright boy -- a pleasure to teach -- but so serious... Brothers should be raised together, Colon. Even brothers from different mothers... 1492: conquest of paradise 1992
L1943 254.0 1.0 m1 u16 COLUMBUS Father, I am doing what I think is the best for him. And he has the teacher I would have chosen for myself. 1492: conquest of paradise 1992
L1947 255.0 0.0 m1 u16 COLUMBUS God... That's in a week! 1492: conquest of paradise 1992
L1948 255.0 1.0 m1 u19 MARCHENA That's what it says. 1492: conquest of paradise 1992
L1949 255.0 2.0 m1 u16 COLUMBUS How did you manage it? 1492: conquest of paradise 1992
L1950 255.0 3.0 m1 u19 MARCHENA With some difficulty. I had to promise them you were not a total fool. 1492: conquest of paradise 1992
L1951 256.0 0.0 m1 u19 MARCHENA Why do you wish to sail west? 1492: conquest of paradise 1992
L1952 256.0 1.0 m1 u16 COLUMBUS To open a new route to Asia. At the moment there are only two ways of reaching it... 1492: conquest of paradise 1992
L1957 257.0 0.0 m1 u19 MARCHENA How can you be so certain? The Ocean is said to be infinite. 1492: conquest of paradise 1992
L1958 257.0 1.0 m1 u16 COLUMBUS Ignorance! I believe the Indies are no more than 750 leagues west of the Canary Islands. 1492: conquest of paradise 1992
L1959 257.0 2.0 m1 u19 MARCHENA How can you be so certain? 1492: conquest of paradise 1992
L1960 257.0 3.0 m1 u16 COLUMBUS The calculations of Toscanelli Marin de Tyr, Esdras... 1492: conquest of paradise 1992
L1961 257.0 4.0 m1 u19 MARCHENA Esdras is a Jew. 1492: conquest of paradise 1992
L1962 257.0 5.0 m1 u16 COLUMBUS So was Christ! 1492: conquest of paradise 1992
L1963 258.0 0.0 m1 u19 MARCHENA Two minutes... and already you're a dead man. Don't let passion overwhelm you, Colon. 1492: conquest of paradise 1992
L1964 258.0 1.0 m1 u16 COLUMBUS I'll try to remember that, Marchena... 1492: conquest of paradise 1992
L1965 258.0 2.0 m1 u19 MARCHENA Father Marchena! 1492: conquest of paradise 1992
L1966 258.0 3.0 m1 u16 COLUMBUS Passion is something one cannot control! 1492: conquest of paradise 1992
L1967 258.0 4.0 m1 u19 MARCHENA You get so carried away when you are being contradicted! 1492: conquest of paradise 1992
L1968 258.0 5.0 m1 u16 COLUMBUS I've been contradicted all my life... Eternity! 1492: conquest of paradise 1992
L1969 258.0 6.0 m1 u19 MARCHENA Only God knows the meaning of such words, my son. 1492: conquest of paradise 1992
L2037 259.0 0.0 m1 u19 MARCHENA You mustn't give way to despair. You must wait. 1492: conquest of paradise 1992
L2038 259.0 1.0 m1 u16 COLUMBUS Wait! I've waited seven years already! How much longer do you want me to wait? 1492: conquest of paradise 1992
L2039 259.0 2.0 m1 u19 MARCHENA If God intends you to go, then you will go. 1492: conquest of paradise 1992
L2040 259.0 3.0 m1 u16 COLUMBUS Damn God! 1492: conquest of paradise 1992
L2041 260.0 0.0 m1 u19 MARCHENA Colon! 1492: conquest of paradise 1992
L2042 260.0 1.0 m1 u16 COLUMBUS Damn all of you! You all set up theories based on what? You never leave the safety of your studies! Go out! Find out what the world is about and then tell me something I can listen to! 1492: conquest of paradise 1992
L2045 261.0 0.0 m1 u16 COLUMBUS All of them! Just lies! 1492: conquest of paradise 1992
L2046 261.0 1.0 m1 u19 MARCHENA Colon! Don't! 1492: conquest of paradise 1992
L2132 262.0 0.0 m1 u19 MARCHENA In Nomine Patris et Filius, et Spiritus Sancti. 1492: conquest of paradise 1992
L2133 262.0 1.0 m1 u16 COLUMBUS Forgive me, Father. For I have sinned. 1492: conquest of paradise 1992
L2134 263.0 0.0 m1 u19 MARCHENA I am listening, my son. 1492: conquest of paradise 1992
L2135 263.0 1.0 m1 u16 COLUMBUS Father, I have betrayed my family. I betrayed my men. And I betrayed you. 1492: conquest of paradise 1992
L2136 263.0 2.0 m1 u19 MARCHENA What are you saying? 1492: conquest of paradise 1992
L2137 263.0 3.0 m1 u16 COLUMBUS I lied. The journey will be longer than I said. 1492: conquest of paradise 1992
L2138 263.0 4.0 m1 u19 MARCHENA How long? 1492: conquest of paradise 1992
L2139 263.0 5.0 m1 u16 COLUMBUS I am not sure... It could be twice the distance. 1492: conquest of paradise 1992
L2140 264.0 0.0 m1 u19 MARCHENA May God forgive you...! You must tell them! You must tell your men! 1492: conquest of paradise 1992
L2141 264.0 1.0 m1 u16 COLUMBUS If I tell them, they won't follow me. You know that I am right, Father. You trust me... 1492: conquest of paradise 1992
L2142 264.0 2.0 m1 u19 MARCHENA My son, my son... Your certitudes are sometimes frightening... Christopher, you must speak to them. And if you don't I will. 1492: conquest of paradise 1992
L2143 264.0 3.0 m1 u16 COLUMBUS You are bound by an oath, Father. 1492: conquest of paradise 1992
L2144 265.0 0.0 m1 u19 MARCHENA I believed in you... 1492: conquest of paradise 1992
L2145 265.0 1.0 m1 u16 COLUMBUS Give me absolution. 1492: conquest of paradise 1992
L2525 266.0 0.0 m1 u19 MARCHENA I suppose we're both old men now. 1492: conquest of paradise 1992
L2526 266.0 1.0 m1 u16 COLUMBUS You'll always be older than me, Father. 1492: conquest of paradise 1992
L2531 267.0 0.0 m1 u16 COLUMBUS I have to disagree. 1492: conquest of paradise 1992
L2532 267.0 1.0 m1 u19 MARCHENA I knew you would. 1492: conquest of paradise 1992
L2533 267.0 2.0 m1 u16 COLUMBUS New worlds create new people. 1492: conquest of paradise 1992
L2534 267.0 3.0 m1 u19 MARCHENA Oh? So you are a new man? 1492: conquest of paradise 1992
L2535 267.0 4.0 m1 u16 COLUMBUS I don't know... I have the impression that I didn't change that much. I still can't accept the world as it is! 1492: conquest of paradise 1992
L2071 268.0 0.0 m1 u18 ISABEL I should not even be listening to you, since my council said no. But Santangel tells me you are a man of honor and sincerity... And Sanchez, that you are not a fool. 1492: conquest of paradise 1992
L2072 268.0 1.0 m1 u16 COLUMBUS No more than the woman who said she would take Granada from the Moors. 1492: conquest of paradise 1992
L2073 269.0 0.0 m1 u18 ISABEL The ocean is uncrossable? 1492: conquest of paradise 1992
L2074 269.0 1.0 m1 u16 COLUMBUS What did they say about Granada before today? 1492: conquest of paradise 1992
L2075 269.0 2.0 m1 u18 ISABEL That she was impregnable. 1492: conquest of paradise 1992
L2076 270.0 0.0 m1 u18 ISABEL I cannot ignore the verdict of my council. 1492: conquest of paradise 1992
L2077 270.0 1.0 m1 u16 COLUMBUS Surely you can do anything you want. 1492: conquest of paradise 1992
L2079 271.0 0.0 m1 u16 COLUMBUS May I speak freely? 1492: conquest of paradise 1992
L2080 271.0 1.0 m1 u18 ISABEL You show no inclination to speak otherwise! 1492: conquest of paradise 1992
L2081 271.0 2.0 m1 u16 COLUMBUS I know what I see. I see someone who doesn't accept the world as it is. Who's not afraid. I see a women who thinks... "What if?"... 1492: conquest of paradise 1992
L2082 271.0 3.0 m1 u18 ISABEL A woman? 1492: conquest of paradise 1992
L2085 272.0 0.0 m1 u18 ISABEL How old are you, Senor Colon? 1492: conquest of paradise 1992
L2086 272.0 1.0 m1 u16 COLUMBUS Thirty seven, Your Majesty... And you? 1492: conquest of paradise 1992
L2279 273.0 0.0 m1 u18 ISABEL Do they have such thoughts? 1492: conquest of paradise 1992
L2280 273.0 1.0 m1 u16 COLUMBUS They come and go as naked as the day God created them... 1492: conquest of paradise 1992
L2456 274.0 0.0 m1 u18 ISABEL But without your brothers. Nor are you to return to Santo Domingo or any of the other colonies. You may explore the continent. 1492: conquest of paradise 1992
L2457 274.0 1.0 m1 u16 COLUMBUS Thank you. 1492: conquest of paradise 1992
L2458 274.0 2.0 m1 u18 ISABEL There is one thing I'd like to understand... Why do you want to go back, after all this? 1492: conquest of paradise 1992
L2459 274.0 3.0 m1 u16 COLUMBUS Your Majesty -- some men are content to read about things. I must see them with my own eyes. I cannot be other than I am. 1492: conquest of paradise 1992
L2277 275.0 0.0 m1 u21 MOXICA And you say this is an Indian vice? By God! I don't see any kind of pleasure that would make this a sin. 1492: conquest of paradise 1992
L2278 275.0 1.0 m1 u16 COLUMBUS The Indians have no such word, Don Moxica. 1492: conquest of paradise 1992
L2328 276.0 0.0 m1 u21 MOXICA We lost cousins, friends. We will wash this in blood. 1492: conquest of paradise 1992
L2329 276.0 1.0 m1 u16 COLUMBUS If you want to keep your head on your shoulders, you'll do as I say. 1492: conquest of paradise 1992
L2331 277.0 0.0 m1 u16 COLUMBUS You want a war? Fine. We are a thousand. They outnumber us by ten! Who will you kill? Which tribe? 1492: conquest of paradise 1992
L2332 277.0 1.0 m1 u21 MOXICA We don't need to know. 1492: conquest of paradise 1992
L2333 277.0 2.0 m1 u16 COLUMBUS We came here to stay! To build! Not to start a crusade. In this forest, there is enough danger to sweep us away in days! So we will be brave and swallow our grief. And in the name of those who died, we will accomplish what we came for. 1492: conquest of paradise 1992
L2345 278.0 0.0 m1 u16 COLUMBUS We can't raise the wheel without it. 1492: conquest of paradise 1992
L2346 278.0 1.0 m1 u21 MOXICA My horse doesn't work. 1492: conquest of paradise 1992
L2347 279.0 0.0 m1 u16 COLUMBUS Don Moxica -- we all have to work. 1492: conquest of paradise 1992
L2348 279.0 1.0 m1 u21 MOXICA You did not hear me, Don Colon. Not my horse. 1492: conquest of paradise 1992
L2386 280.0 0.0 m1 u16 COLUMBUS In one act of brutality, you have created chaos. Tribes who were fighting each other are now joining forces against us! All that because of your criminal savagery! 1492: conquest of paradise 1992
L2387 280.0 1.0 m1 u21 MOXICA Savagery is what monkeys understand. 1492: conquest of paradise 1992
L2388 280.0 2.0 m1 u16 COLUMBUS You'll be held in detention, deprived of your privileges and possessions. Until you are returned to Spain where you will be judged. Have you anything to say? 1492: conquest of paradise 1992
L2389 280.0 3.0 m1 u21 MOXICA You will regret this. 1492: conquest of paradise 1992
L2154 281.0 0.0 m1 u16 COLUMBUS Due west, Captain Mendez. And may God be with us... 1492: conquest of paradise 1992
L2155 281.0 1.0 m1 u20 MENDEZ God be with us admiral. 1492: conquest of paradise 1992
L2157 282.0 0.0 m1 u20 MENDEZ Well... It's the men, Sir. They wonder how you know our position. We've lost sight from land days ago... 1492: conquest of paradise 1992
L2158 282.0 1.0 m1 u16 COLUMBUS And what do you think Mendez? 1492: conquest of paradise 1992
L2159 282.0 2.0 m1 u20 MENDEZ Well, I surely know what a quadrant is! But I've never seen it used at night before. 1492: conquest of paradise 1992
L2160 282.0 3.0 m1 u16 COLUMBUS Come over here. 1492: conquest of paradise 1992
L2164 283.0 0.0 m1 u16 COLUMBUS What do you read? 1492: conquest of paradise 1992
L2165 283.0 1.0 m1 u20 MENDEZ Twenty eight. 1492: conquest of paradise 1992
L2490 284.0 0.0 m1 u20 MENDEZ What's he doing? 1492: conquest of paradise 1992
L2491 284.0 1.0 m1 u16 COLUMBUS He's drawing an isthmus... He's saying we're on an isthmus. 1492: conquest of paradise 1992
L2492 284.0 2.0 m1 u20 MENDEZ We can't be. 1492: conquest of paradise 1992
L2064 285.0 0.0 m1 u16 COLUMBUS Where can I meet this man? 1492: conquest of paradise 1992
L2065 285.0 1.0 m1 u22 PINZON Immediately. 1492: conquest of paradise 1992
L2196 286.0 0.0 m1 u22 PINZON You lied! You cheated! We're way past 750 leagues! 1492: conquest of paradise 1992
L2197 286.0 1.0 m1 u16 COLUMBUS Six days ago, yes. 1492: conquest of paradise 1992
L2198 286.0 2.0 m1 u22 PINZON You must be mad...! 1492: conquest of paradise 1992
L2199 286.0 3.0 m1 u16 COLUMBUS We have to keep the hopes of these men alive! 1492: conquest of paradise 1992
L2200 286.0 4.0 m1 u22 PINZON We're on the verge of a mutiny, Colon! 1492: conquest of paradise 1992
L2201 286.0 5.0 m1 u16 COLUMBUS You think I don't know that? 1492: conquest of paradise 1992
L2202 286.0 6.0 m1 u22 PINZON We're lost! 1492: conquest of paradise 1992
L2203 286.0 7.0 m1 u16 COLUMBUS The land is there. I know it! 1492: conquest of paradise 1992
L2204 286.0 8.0 m1 u22 PINZON You don't know anything! Listen Colon, these are my ships, right? So I'm telling you we're turning back! 1492: conquest of paradise 1992
L2205 286.0 9.0 m1 u16 COLUMBUS And then what? Half of the water has gone, the rest is nearly putrid! You know that! 1492: conquest of paradise 1992
L2206 286.0 10.0 m1 u22 PINZON Jesus Maria! I should have never listened to you! 1492: conquest of paradise 1992
L2207 286.0 11.0 m1 u16 COLUMBUS You never did. You did all the talking for both of us, remember? 1492: conquest of paradise 1992
L2208 286.0 12.0 m1 u22 PINZON You bloody... 1492: conquest of paradise 1992
L2209 286.0 13.0 m1 u16 COLUMBUS Pinzon, Pinzon... All we can do now is go forward! Think about that! 1492: conquest of paradise 1992
L2210 286.0 14.0 m1 u22 PINZON You tell that to them! 1492: conquest of paradise 1992
L2211 286.0 15.0 m1 u16 COLUMBUS You're right. Let the men decide. 1492: conquest of paradise 1992
L1930 287.0 0.0 m1 u18 ISABEL Is that the man I knew, Treasurer Sanchez? 1492: conquest of paradise 1992
L1931 287.0 1.0 m1 u24 SANCHEZ Yes, Your Majesty. 1492: conquest of paradise 1992
L2112 288.0 0.0 m1 u18 ISABEL You were right, Don Sanchez... His demands could never be granted. 1492: conquest of paradise 1992
L2113 288.0 1.0 m1 u24 SANCHEZ Never, Your Majesty. Although... 1492: conquest of paradise 1992
L2115 289.0 0.0 m1 u24 SANCHEZ ... Into a monk... 1492: conquest of paradise 1992
L2116 289.0 1.0 m1 u18 ISABEL Yes. It would be a pity, wouldn't it? Call him back! 1492: conquest of paradise 1992
L2376 290.0 0.0 m1 u24 SANCHEZ Every ship returns with a cargo of sick and dying. But with no gold! The new world proves expensive, Your Majesty. 1492: conquest of paradise 1992
L2377 290.0 1.0 m1 u18 ISABEL We weren't expecting immediate profits, were we? We must have faith. We must give time for time. 1492: conquest of paradise 1992
L2414 291.0 0.0 m1 u24 SANCHEZ ... But there is worse. He ordered the execution of five members of the nobility... 1492: conquest of paradise 1992
L2415 291.0 1.0 m1 u18 ISABEL Is this true, Brother Buyl? 1492: conquest of paradise 1992
L2417 292.0 0.0 m1 u18 ISABEL Then, what do you suggest, Don Sanchez? 1492: conquest of paradise 1992
L2418 292.0 1.0 m1 u24 SANCHEZ He must be replaced. 1492: conquest of paradise 1992
L2419 292.0 2.0 m1 u18 ISABEL And who would you think of, for such a task? 1492: conquest of paradise 1992
L2460 293.0 0.0 m1 u18 ISABEL I know, I should not tolerate his impertinence. 1492: conquest of paradise 1992
L2461 293.0 1.0 m1 u24 SANCHEZ Then why? 1492: conquest of paradise 1992
L2462 293.0 2.0 m1 u18 ISABEL Because he is not afraid of me. 1492: conquest of paradise 1992
L3380 294.0 0.0 m2 u30 EMIL Are you my attorney? I'm Emil. I'm insane. 15 minutes 2001
L3381 294.0 1.0 m2 u26 CUTLER I'm not your lawyer until I see the money. 15 minutes 2001
L3382 294.0 2.0 m2 u30 EMIL Here. I have your money. 15 minutes 2001
L3383 295.0 0.0 m2 u30 EMIL Oh no! No! Shit! 15 minutes 2001
L3384 295.0 1.0 m2 u26 CUTLER Emil. Take it easy. Stay with me. Sit down. What do you need? What are you looking for? 15 minutes 2001
L3385 295.0 2.0 m2 u30 EMIL He has the camera! He took the movie! 15 minutes 2001
L3392 296.0 0.0 m2 u26 CUTLER Don't say anything. 15 minutes 2001
L3393 296.0 1.0 m2 u30 EMIL Where are we going? 15 minutes 2001
L3394 296.0 2.0 m2 u26 CUTLER I'm coming with you. 15 minutes 2001
L3395 296.0 3.0 m2 u30 EMIL Yes. Yes, come with me! 15 minutes 2001
L3396 296.0 4.0 m2 u26 CUTLER I'm invoking rights - this man is represented by counsel. I'm coming with him. 15 minutes 2001
L3459 297.0 0.0 m2 u26 CUTLER I brought you some letters. It's really fan mail. Women mostly. One wants to buy you clothes, another sent a check. Another wants a check. 15 minutes 2001
L3460 297.0 1.0 m2 u30 EMIL You bring the cigarettes? 15 minutes 2001
L3461 297.0 2.0 m2 u26 CUTLER Oh, sure. 15 minutes 2001
L3464 298.0 0.0 m2 u26 CUTLER ...delusions and paranoia. 15 minutes 2001
L3465 298.0 1.0 m2 u30 EMIL I was all of these. 15 minutes 2001
L3466 298.0 2.0 m2 u26 CUTLER Well, you didn't appreciate the severity of it until recently. No question about that. 15 minutes 2001
L3467 298.0 3.0 m2 u30 EMIL What about Oleg? 15 minutes 2001
L3468 298.0 4.0 m2 u26 CUTLER Disappeared. They're looking everywhere. Maybe he went back to Czechoslovakia. 15 minutes 2001
L3469 298.0 5.0 m2 u30 EMIL No, he is here. Shit... 15 minutes 2001
L3470 298.0 6.0 m2 u26 CUTLER Don't worry about him. Think about yourself. 15 minutes 2001
L3471 298.0 7.0 m2 u30 EMIL What about my movie rights? Book rights? 15 minutes 2001
L3472 298.0 8.0 m2 u26 CUTLER Look, I haven't really focused on that kind of thing. 15 minutes 2001
L3473 298.0 9.0 m2 u30 EMIL What's your cut? How much? 15 minutes 2001
L3474 298.0 10.0 m2 u26 CUTLER I would say...half. Half is fair. 15 minutes 2001
L3475 298.0 11.0 m2 u30 EMIL No. No way. 15 minutes 2001
L3476 298.0 12.0 m2 u26 CUTLER But it's... 15 minutes 2001
L3477 298.0 13.0 m2 u30 EMIL Thirty-percent. No more. Or I call another lawyer. This is the biggest case of your life. Don't try to negotiate. Thirty percent. Say yes or no. 15 minutes 2001
L3478 298.0 14.0 m2 u26 CUTLER This is not about money, Emil. I need your trust in me. 15 minutes 2001
L3479 298.0 15.0 m2 u30 EMIL What else do you need? 15 minutes 2001
L3480 298.0 16.0 m2 u26 CUTLER I need to know about your background. I need to know about your upbringing. Why you're here. 15 minutes 2001
L3481 298.0 17.0 m2 u30 EMIL Give me another one, please. 15 minutes 2001
L3482 299.0 0.0 m2 u26 CUTLER Tell me about yourself. What you did as a young boy... what your parents were like. 15 minutes 2001
L3483 299.0 1.0 m2 u30 EMIL My father always degraded me. Killed my self-esteem. And my mother was blind. 15 minutes 2001
L3484 299.0 2.0 m2 u26 CUTLER Your mother was blind? 15 minutes 2001
L3485 299.0 3.0 m2 u30 EMIL Yeah, she went blind giving birth to me. She went to fucking black market doctor to induce me. 15 minutes 2001
L3486 299.0 4.0 m2 u26 CUTLER Back in the Czech Republic? 15 minutes 2001
L3487 299.0 5.0 m2 u30 EMIL Yeah, yeah...bad doctor gave her bad drugs which made her go blind. And my father blamed me for her blindness... 15 minutes 2001
L3488 299.0 6.0 m2 u26 CUTLER Your father blamed you for your mother's blindness? 15 minutes 2001
L3489 299.0 7.0 m2 u30 EMIL Yeah, he hated me from day when I was born. Put it out. Can you put the cigarette out? 15 minutes 2001
L3490 300.0 0.0 m2 u30 EMIL That's what he did to me. He put cigarettes out on me. 15 minutes 2001
L3491 300.0 1.0 m2 u26 CUTLER Your father put cigarettes out on you? 15 minutes 2001
L3492 300.0 2.0 m2 u30 EMIL Out on my back when I was a small boy. 15 minutes 2001
L3493 300.0 3.0 m2 u26 CUTLER Can I see your back? 15 minutes 2001
L3496 301.0 0.0 m2 u30 EMIL I'm abused. Don't you think? 15 minutes 2001
L3497 301.0 1.0 m2 u26 CUTLER I don't think it's abuse, I think it's torture. 15 minutes 2001
L3545 302.0 0.0 m2 u30 EMIL ...so we kill someone famous and if we are caught, we are sent to mental hospital... 15 minutes 2001
L3546 302.0 1.0 m2 u26 CUTLER Officers, there's your killer, do your duty, arrest him! 15 minutes 2001
L3059 303.0 0.0 m2 u27 DAPHNE ...my little sister and I shared a flat - I came home one night and a man was raping her. His gun was on the chair... He came at me and I shot him. 15 minutes 2001
L3060 303.0 1.0 m2 u34 JORDY Alright. That's a justifiable homicide. 15 minutes 2001
L3061 303.0 2.0 m2 u27 DAPHNE Yes, but he was a cop. 15 minutes 2001
L3089 304.0 0.0 m2 u27 DAPHNE Now I become custody of police department? 15 minutes 2001
L3090 304.0 1.0 m2 u34 JORDY If you cooperate with the DA - maybe they'll help you with your situation. 15 minutes 2001
L3091 304.0 2.0 m2 u27 DAPHNE I will if they don't send me back. 15 minutes 2001
L3092 304.0 3.0 m2 u34 JORDY They won't until this is over. 15 minutes 2001
L3093 305.0 0.0 m2 u27 DAPHNE Are you married? 15 minutes 2001
L3094 305.0 1.0 m2 u34 JORDY Divorced. 15 minutes 2001
L3095 305.0 2.0 m2 u27 DAPHNE Do you live alone? I've been in these clothes since...the killings. Could we stop at your place? I could take a shower...before I go into custody? 15 minutes 2001

Let's amalgamate the texts utered in the same conversations together.

By doing this we loose all the information in the order of utterance.

But this is fine as we are going to do LDA with just the first-order information of words uttered in each conversation by anyone involved in the dialogue.

import org.apache.spark.sql.functions.{collect_list, udf, lit, concat_ws}

val corpusDF = convLines.groupBy($"conversationID",$"movieID")
  .agg(concat_ws(" :-()-: ",collect_list($"text")).alias("corpus"))
  .join(moviesMetaData, "movieID") // and join it to get movie meta data
  .select($"conversationID".as("id"),$"corpus",$"movieTitle",$"movieYear")
  .cache()
import org.apache.spark.sql.functions.{collect_list, udf, lit, concat_ws}
corpusDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint, corpus: string ... 2 more fields]
corpusDF.count()
res28: Long = 83097
corpusDF.take(5).foreach(println)
[28762,Your wife and children...you're happy with them? :-()-: Yes. :-()-: Good.,the godfather,1972]
[28815,Michael? :-()-: I'm thinking about it. :-()-: Michael... :-()-: No, I would not like you better if you were Ingrid Bergman.,the godfather,1972]
[28842,What is it? :-()-: Is it all right if I go to the bathroom?,the godfather,1972]
[28766,Things went badly in Palermo? :-()-: The younger men have no respect. Things are changing; I don't know what will happen.  Michael, because of the wedding, people now know your name. :-()-: Is that why there are more men on the walls? :-()-: Even so, I don't think it is safe here anymore.  I've made plans to move you to a villa near Siracuse. You must go right away. :-()-: What is it? :-()-: Bad news from America.  Your brother, Santino.  He has been killed.,the godfather,1972]
[28835,We can't wait.  No matter what Sollozzo say about a deal, he's figuring out how to kill Pop.  You have to get Sollozzo now. :-()-: The kid's right.,the godfather,1972]
display(corpusDF)
id corpus movieTitle movieYear
28762.0 Your wife and children...you're happy with them? :-()-: Yes. :-()-: Good. the godfather 1972
28815.0 Michael? :-()-: I'm thinking about it. :-()-: Michael... :-()-: No, I would not like you better if you were Ingrid Bergman. the godfather 1972
28842.0 What is it? :-()-: Is it all right if I go to the bathroom? the godfather 1972
28766.0 Things went badly in Palermo? :-()-: The younger men have no respect. Things are changing; I don't know what will happen. Michael, because of the wedding, people now know your name. :-()-: Is that why there are more men on the walls? :-()-: Even so, I don't think it is safe here anymore. I've made plans to move you to a villa near Siracuse. You must go right away. :-()-: What is it? :-()-: Bad news from America. Your brother, Santino. He has been killed. the godfather 1972
28835.0 We can't wait. No matter what Sollozzo say about a deal, he's figuring out how to kill Pop. You have to get Sollozzo now. :-()-: The kid's right. the godfather 1972
28717.0 Jesus, Connie...Sure, Mike... :-()-: Go back to your house and wait for me... the godfather 1972
28735.0 I tol' you to stay put, Paulie... :-()-: The guy at the gate's outside...says there's a package... the godfather 1972
28752.0 What is this nonsense? :-()-: It's from Johnny. It was announced this morning. He's going to play the lead in the new Woltz Brothers film. the godfather 1972
28852.0 You straightened my brother out? :-()-: Hell, he was banging cocktail waitresses two at a time. Players couldn't get a drink. the godfather 1972
28782.0 Tom, you're the Consigliere, what do we do if the old man dies? :-()-: Without your father's political contacts and personal influence, the Corleone family loses half its strength. Without your father, the other New York families might wind up supporting Sollozzo, and the Tattaglias just to make sure there isn't a long destructive war. The old days are over, this is 1946; nobody wants bloodshed anymore. If your father dies...make the deal, Sonny. :-()-: That's easy to say; it's not your father. :-()-: I was as good a son to him as you or Mike. :-()-: Oh Christ Tom, I didn't mean it that way. :-()-: We're all tired... :-()-: OK, we sit tight until the old man can give us the lead. But Tom, I want you to stay inside the Mall. You too, Mike, no chances. Tessio, you hold your people in reserve, but have them nosing around the city. The hospital is yours; I want it tight, fool-proof, 24 hours a day. the godfather 1972
28840.0 We're going to New Jersey? :-()-: Maybe. the godfather 1972
28729.0 Sollozzo knows Mike's a civilian. :-()-: OK, but be careful. the godfather 1972
28730.0 I want somebody very good, very safe to plant that gun. I don't want my brother coming out of that toilet with just his dick in his hand. :-()-: The gun will be there. :-()-: You're on, kid...I'll square it with Mom your not seeing her before you left. And I'll get a message to your girl friend when I think the time is right. :-()-: We gotta move... the godfather 1972
28774.0 Ever seen anything like that before? :-()-: No. the godfather 1972
28724.0 The food is on the table. :-()-: I'm not hungry yet. :-()-: Eat it, it's on the table. :-()-: Ba Fa Goulle. :-()-: BA FA GOULE YOU! the godfather 1972
28839.0 I'm glad you came, Mike. I hope we can straighten everything out. All this is terrible, it's not the way I wanted things to happen at all. It should never have happened. :-()-: I want to settle things tonight. I want my father left alone. :-()-: He won't be; I swear to you be my children he won't be. Just keep an open mind when we talk. I hope you're not a hothead like your brother, Sonny. It's impossible to talk business with him. the godfather 1972
28827.0 You cannot stay here...I'm sorry. :-()-: You and I are going to move my father right now...to another room on another floor...Can you disconnect those tubes so we can wheel the bed out? :-()-: Absolutely not! We have to get permission from the Doctor. :-()-: You've read about my father in the papers. You've seen that no one's here to guard him. Now I've just gotten word that men are coming to this hospital to kill him. Believe me and help me. :-()-: We don't have to disconnect them, we can wheel the stand with the bed. the godfather 1972
28721.0 What's the matter, Carlo? :-()-: Shut up. the godfather 1972
28820.0 When will I see you again? :-()-: Goodbye. the godfather 1972
28843.0 Do you pledge to guide and protect this child if he is left fatherless? Do you promise to shield him against the wickedness of the world? :-()-: Yes, I promise. the godfather 1972
28816.0 Hello. Kay? :-()-: How is your father? :-()-: He'll be OK. :-()-: I love you. the godfather 1972
28751.0 When does my daughter leave with her bridegroom? :-()-: They'll cut the cake in a few minutes...leave right after that. Your new son-in-law, do we give him something important? :-()-: No, give him a living. But never let him know the family's business. What else, Tom? :-()-: I've called the hospital; they've notified Consiglere Genco's family to come and wait. He won't last out the night. the godfather 1972
28728.0 You take care of Paulie? :-()-: You won't see Paulie anymore. He's sick for good this winter. the godfather 1972
28744.0 All right, Hollywood...Now tell me about this Hollywood Pezzonovanta who won't let you work. :-()-: He owns the studio. Just a month ago he bought the movie rights to this book, a best seller. And the main character is a guy just like me. I wouldn't even have to act, just be myself. the godfather 1972
28780.0 Is the hospital covered? :-()-: The cops have it locked in and I got my people there visiting Pop all the time. What about the hit list. the godfather 1972
28781.0 What about Luca? Sollozzo didn't seem worried about Luca. That worries me. :-()-: If Luca sold out we're in real trouble. :-()-: Has anyone been able to get in touch with him? :-()-: No, and I've been calling all night. Maybe he's shacked up. :-()-: Luca never sleeps over with a broad. He always goes home when he's through. Mike, keep ringing Luca's number. the godfather 1972
28785.0 I found out about this Captain McCluskey who broke Mike's jaw. He's definitely on Sollozzo's payroll, and for big money. McCluskey's agreed to be the Turk's bodyguard. What you have to understand is that while Sollozzo is guarded like this, he's invulnerable. Nobody has ever gunned down a New York Police Captain. Never. It would be disastrous. All the five families would come after you Sonny; the Corleone family would be outcasts; even the old man's political protection would run for cover. So just...take that into consideration. :-()-: McCluskey can't stay with the Turk forever. We'll wait. the godfather 1972
28761.0 Barzini will move against you first. :-()-: How? :-()-: He will get in touch with you through someone you absolutely trust. That person will arrange a meeting, guarantee your safety... the godfather 1972
28738.0 Good for ten men... :-()-: OK, go to Arthur Avenue; I'm suppose to call when I found somethin'. the godfather 1972
28803.0 I've never seen anything like it. :-()-: I told you I had a lot of relatives. the godfather 1972
28836.0 Go on Mike. :-()-: They want me to go to the conference with Sollozzo. Set up the meeting for two days from now. Sonny, get our informers to find out where the meeting will be held. Insist it has to be a public place: a bar or restaurant at the height of the dinner hour. So I'll feel safe. They'll check me when I meet them so I won't be able to carry a weapon; but Clemenza, figure out a way to have one planted there for me. Then I'll kill them both. the godfather 1972
28720.0 Don't be frightened. Do you think I'd make my sister a widow? Do you think I'd make your children fatherless? After all, I'm Godfather to your son. No, your punishment is that you're out of the family business. I'm putting you on a plane to Vegas--and I want you to stay there. I'll send Connie an allowance, that's all. But don't keep saying you're innocent; it insults my intelligence and makes me angry. Who approached you, Tattaglia or Barzini? :-()-: Barzini. :-()-: Good, good. Leave now; there's a car waiting to take you to the airport. the godfather 1972
28745.0 You take care of your family? :-()-: Sure. the godfather 1972
28753.0 My wife was weeping before she fell asleep, outside my window I saw my caporegimes to the house, and it is midnight. So, Consigliere of mine, I think you should tell your Don what everyone knows. :-()-: I didn't tell Mama anything. I was about to come up and wake you and tell you. Just now. :-()-: But you needed a drink first. :-()-: Yes. :-()-: Now you've had your drink. the godfather 1972
28767.0 You tell us about America. :-()-: How do you know I come from America? :-()-: We hear. We were told you were a Pezzonovanta...big shot. :-()-: Only the son of a Pezzonovanta. :-()-: Hey America! Is she as rich as they say? :-()-: Yes. :-()-: Take me to America! You need a good lupara in America? You take me, I'll be the best man you got. "Oh say, can you seeee...By da star early light..." the godfather 1972
28797.0 Hello Kay. Your father's inside, doing some business. He's been asking for you. :-()-: Thanks Tom. the godfather 1972
28814.0 Would you like me better if I were a nun? :-()-: No. :-()-: Would you like me better if I were Ingrid Bergman? the godfather 1972
28817.0 I LOVE YOU. :-()-: Yeah Kay, I'm here. :-()-: Can you say it? :-()-: Huh? :-()-: Tell me you love me. the godfather 1972
28845.0 Do you wish to be baptized? :-()-: I do wish to be baptized. the godfather 1972
28794.0 Sonny was hot for my deal, right? You know it's the smart thing to do, too. I want you to talk Sonny into it. :-()-: Sonny will come after you with everything he's got. the godfather 1972
28758.0 Have you thought about a wife? A family? :-()-: No. :-()-: I understand, Michael. But you must make a family, you know. :-()-: I want children, I want a family. But I don't know when. :-()-: Accept what's happened, Michael. :-()-: I could accept everything that's happened; I could accept it, but that I never had a choice. From the time I was born, you had laid this all out for me. :-()-: No, I wanted other things for you. :-()-: You wanted me to be your son. :-()-: Yes, but sons who would be professors, scientists, musicians...and grandchildren who could be, who knows, a Governor, a President even, nothing's impossible here in America. :-()-: Then why have I become a man like you? :-()-: You are like me, we refuse to be fools, to be puppets dancing on a string pulled by other men. I hoped the time for guns and killing and massacres was over. That was my misfortune. That was your misfortune. I was hunted on the streets of Corleone when I was twelve years old because of who my father was. I had no choice. :-()-: A man has to choose what he will be. I believe that. :-()-: What else do you believe in? the godfather 1972
28809.0 If he's your brother, why does he have a different name? :-()-: My brother Sonny found him living in the streets when he was a kid, so my father took him in. He's a good lawyer. the godfather 1972
28807.0 I never know when you're telling me the truth. :-()-: I told you you wouldn't like him. :-()-: He's coming over here! the godfather 1972
28719.0 You fingered Sonny for the Barzini people. That little farce you played out with my sister. Did Barzini kid you that would fool a Corleone? :-()-: I swear I'm innocent. I swear on the head of my children, I'm innocent. Mike, don't do this to me, please Mike, don't do this to me! :-()-: Barzini is dead. So is Philip Tattaglia, so are Strachi, Cuneo and Moe Greene...I want to square all the family accounts tonight. So don't tell me you're innocent; admit what you did. the godfather 1972
28798.0 Sure. Anything I can do for you. :-()-: No. I guess I'll see you Christmas. Everyone's going to be out at Long Beach, right? :-()-: Right. the godfather 1972
28792.0 Will you give this to him. :-()-: If I accept that letter and you told a Court of Law I accepted it, they would interpret it as my having knowledge of his whereabouts. Just wait Kay, he'll contact you. the godfather 1972
28822.0 Your sister wants to ask you something. :-()-: Let HER ask. the godfather 1972
28784.0 Was there a definite proposal? :-()-: Sure, he wants us to send Mike to meet him to hear his proposition. The promise is the deal will be so good we can't refuse. :-()-: What about that Tattaglias? What will they do about Bruno? :-()-: Part of the deal: Bruno cancels out what they did to my father. :-()-: We should hear what they have to say. :-()-: No, no Consiglere. Not this time. No more meetings, no more discussions, no more Sollozzo tricks. Give them one message: I WANT SOLLOZZO. If not, it's all out war. We go to the mattresses and we put all the button men out on the street. :-()-: The other families won't sit still for all out war. :-()-: Then THEY hand me Sollozzo. :-()-: Come ON Sonny, your father wouldn't want to hear this. This is not a personal thing, this is Business. :-()-: And when they shot me father... :-()-: Yes, even the shooting of your father was business, not personal... :-()-: No no, no more advice on how to patch it up Tom. You just help me win. Understood? the godfather 1972
28846.0 Barzini's people chisel my territory and we do nothing about it. Pretty soon there won't be one place in Brooklyn I can hang my hat. :-()-: Just be patient. :-()-: I'm not asking you for help, Mike. Just take off the handcuffs. :-()-: Be patient. the godfather 1972
28786.0 One of Tattaglia's people? :-()-: No. Our informer in McCluskey's precinct. Tonight at 8:00 he signed out for Louis' Restaurant in the Bronx. Anyone know it. the godfather 1972
28754.0 When I meet with Tattaglia's people; should I insist that all his drug middle-men be clean? :-()-: Mention it, don't insist. Barzini is a man who will know that without being told. :-()-: You mean Tattaglia. :-()-: Barzini. :-()-: He was the one behind Sollozzo? :-()-: Tattaglia is a pimp. He could never have outfought Santino. But I wasn't sure until this day. No, it was Barzini all along. the godfather 1972
28760.0 I see you have your Luca Brasi. :-()-: I'll need him. :-()-: There are men in this world who demand to be killed. They argue in gambling games; they jump out of their cars in a rage if someone so much as scratches their fender. These people wander through the streets calling out "Kill me, kill me." Luca Brasi was like that. And since he wasn't scared of death, and in fact, looked for it...I made him my weapon. Because I was the only person in the world that he truly hoped would not kill him. I think you have done the same with this man. the godfather 1972
28802.0 Christ, Tom; I needed more time with him. I really needed him. :-()-: Did he give you his politicians? :-()-: Not all...I needed another four months and I would have had them all. I guess you've figured it all out? :-()-: How will they come at you? :-()-: I know now. I'll make them call me Don. :-()-: Have you agreed on a meeting? :-()-: A week from tonight. In Brooklyn on Tessio's ground, where I'll be safe. the godfather 1972
28771.0 It's the real Thunderbolt, then. :-()-: Come Sunday morning: My name is Vitelli and my house is up there on the hill, above the village. the godfather 1972
28829.0 All right, Mikey...who do we have to hit, Clemenza or Paulie? :-()-: What? :-()-: One of them fingered the old man. the godfather 1972
28795.0 The Don was slipping; in the old days I could never have gotten to him. Now he's dead, nothing can bring him back. Talk to Sonny, talk to the Caporegimes, Clemenza and Tessio...it's good business. :-()-: Even Sonny won't be able to call off Luca Brasi. :-()-: I'll worry about Luca. You take care of Sonny and the other two kids. :-()-: I'll try...It's what the Don would want us to do. :-()-: Good...then you can go... I don't like violence. I'm a businessman, and blood is a big expense. the godfather 1972
28715.0 What do you wish me to do? :-()-: I want you to use all your powers, all your skill, as you love me. I do not want his mother to see him as he is. the godfather 1972
28757.0 What was this for? :-()-: For bravery. :-()-: And this? :-()-: For killing a man. :-()-: What miracles you do for strangers. :-()-: I fought for my country. It was my choice. :-()-: And now, what do you choose to do? :-()-: I'm going to finish school. :-()-: Good. When you are finished, come and talk to me. I have hopes for you. the godfather 1972
28741.0 My compliments. I'll take care of them from my share. :-()-: So. I receive 30 per cent just for finance and legal protection. No worries about operations, is that what you tell me? :-()-: If you think two million dollars in cash is just finance, I congratulate you Don Corleone. the godfather 1972
28851.0 The Corleone family wants to buy me out. I buy you out. You don't buy me out. :-()-: Your casino loses money. Maybe we can do better. :-()-: You think I scam? :-()-: You're unlucky. :-()-: You goddamn dagos. I do you a favor and take Freddie in when you're having a bad time, and then you try to push me out. :-()-: You took Freddie in because the Corleone family bankrolled your casino. You and the Corleone family are evened out. This is for business; name your price. :-()-: The Corleone family don't have that kind of muscle anymore. The Godfather is sick. You're getting chased out of New York by Barzini and the other families, and you think you can find easier pickings here. I've talked to Barzini; I can make a deal with him and keep my hotel! :-()-: Is that why you thought you could slap Freddie around in public? the godfather 1972
28844.0 Do you renounce Satan. :-()-: I do renounce him. :-()-: And all his works? :-()-: I do renounce them. the godfather 1972
28825.0 Michael, it's not true. Please tell me. :-()-: Don't ask me. :-()-: Tell me! :-()-: All right, this one time I'll let you ask about my affairs, one last time. :-()-: Is it true? the godfather 1972
28841.0 Most important...I want a sure guarantee that no more attempts will be made on my father's life. :-()-: What guarantees can I give you? I am the hunted one. I've missed my chance. You think too highly of me, my friend...I am not so clever...all I want if a truce... the godfather 1972
28789.0 We'll let the old man take it easy for a couple of weeks. I want to get things going good before he gets better. What's the matter with you? :-()-: You start operating, the five families will start their raids again. We're at a stalemate Sonny, your war is costing us a lot of money. :-()-: No more stalemate Tom, we got the soldiers, we'll match them gun for gun if that's how they want it. They know me for what I am, Tom-- and they're scared of me. :-()-: Yes. That's true, you're getting a hell of a reputation. :-()-: Well it's war! We might not be in this shape if we had a real war- time Consiglere, a Sicilian. Pop had Genco, who do I have? Hey Tom, hey...hey. It's Sunday, we're gonna have dinner. Don't be sore. the godfather 1972
28812.0 What will your father say? :-()-: As long as I tell him beforehand he won't object. He'll be hurt, but he won't object. :-()-: What time do they expect us? :-()-: For dinner. Unless I call and tell them we're still in New Hampshire. :-()-: Michael. :-()-: Then we can have dinner, see a show, and spend one more night. the godfather 1972
28793.0 Will you give this letter to Michael. :-()-: Mama, no. the godfather 1972
28726.0 You heard about your father? :-()-: Yeah. :-()-: The word is out in the streets that he's dead. :-()-: Where the hell was Paulie, why wasn't he with the Don? :-()-: Paulie's been a little sick all winter...he was home. :-()-: How many times did he stay home the last couple of months? :-()-: Maybe three, four times. I always asked Freddie if he wanted another bodyguard, but he said no. Things have been so smooth the last ten years... :-()-: Go get Paulie, I don't care how sick he is. Pick him up yourself, and bring him to my father's house. :-()-: That's all? Don't you want me to send some people over here? :-()-: No, just you and Paulie. the godfather 1972
28788.0 Pop, they hit us and we hit them back. :-()-: We put out a lot of material through our contacts in the Newspapers...about McCluskey's being tied up with Sollozzo in the Drug Rackets...things are starting to loosen up. the godfather 1972
28716.0 Understood. I just wish I was doing more to help out. :-()-: I'll come to you when I need you. the godfather 1972
28783.0 Maybe Mike shouldn't get mixed up in this so directly. You know the old man doesn't want that. :-()-: OK forget it, just stay on the phone. the godfather 1972
28736.0 Outside. :-()-: Sure. the godfather 1972
28773.0 You look wonderful, kid; really wonderful. That doctor did some job on your face. :-()-: You look good, too. the godfather 1972
28776.0 Who are those girls? :-()-: That's for you to find out. :-()-: Give them some money and send them home. :-()-: Mike! :-()-: Get rid of them... the godfather 1972
28810.0 I didn't know your family knew Johnny Fontane. :-()-: Sure. :-()-: I used to come down to New York whenever he sang at the Capitol and scream my head off. :-()-: He's my father's godson; he owes him his whole career. the godfather 1972
28804.0 Michael, what are those men doing? :-()-: They're waiting to see my father. :-()-: They're talking to themselves. :-()-: They're going to talk to my father, which means they're going to ask him for something, which means they better get it right. :-()-: Why do they bother him on a day like this? :-()-: Because they know that no Sicilian will refuse a request on his daughter's wedding day. the godfather 1972
28787.0 Jesus, I don't know... :-()-: Can you do it Mike? the godfather 1972
28833.0 Sonny...Sonny--Jesus Christ, I'm down at the hospital. I came down late. There's no one here. None of Tessio's people--no detectives, no one. The old man is completely unprotected. :-()-: All right, get him in a different room; lock the door from the inside. I'll have some men there inside of fifteen minutes. Sit tight, and don't panic. :-()-: I won't panic. the godfather 1972
28747.0 Is it necessary? :-()-: You understand him better than anyone. the godfather 1972
28849.0 Mike, good to see you. Got everything you want? :-()-: Thanks. :-()-: The chef cooked for you special; the dancers will kick your tongue out and you credit is good! Draw chips for all these people so they can play on the house. :-()-: Is my credit good enough to buy you out? the godfather 1972
28834.0 Mikey, you look beautiful! :-()-: Cut it out. :-()-: The Turk wants to talk! The nerve of that son of a bitch! After he craps out last night he wants a meet. the godfather 1972
28714.0 Be my friend. :-()-: Good. From me you'll get Justice. :-()-: Godfather. :-()-: Some day, and that day may never come, I would like to call upon you to do me a service in return. the godfather 1972
28768.0 Hey, beautiful girls! :-()-: Shhhhh. the godfather 1972
28821.0 I have to see my father and his people when we get back to the Mall. :-()-: Oh Michael. :-()-: We'll go to the show tomorrow night--we can change the tickets. :-()-: Don't you want dinner first? :-()-: No, you eat...don't wait up for me. :-()-: Wake me up when you come to bed? the godfather 1972
28718.0 Godfather! :-()-: You have to answer for Santino. the godfather 1972
28750.0 It is Johnny. He came all the way from California to be at the wedding. :-()-: Should I bring him in. :-()-: No. Let the people enjoy him. You see? He is a good godson. :-()-: It's been two years. He's probably in trouble again. the godfather 1972
28847.0 Let us fill up our Regimes. :-()-: No. I want things very calm for another six months. :-()-: Forgive me, Godfather, let our years of friendship be my excuse. How can you hope for success there without your strength here to back you up? The two go hand in hand. And with you gone from here the Barzini and the Tattaglias will be too strong for us. the godfather 1972
28791.0 What was that? :-()-: An accident. No one was hurt. :-()-: Listen Tom, I let my cab go; can I come in to call another one? the godfather 1972
28763.0 ...a fine boy from Sicily, captured by the American Army, and sent to New Jersey as a prisoner of war... :-()-: Nazorine, my friend, tell me what I can do. :-()-: Now that the war is over, Enzo, this boy is being repatriated to Italy. And you see, Godfather... He...my daughter...they... :-()-: You want him to stay in this country. :-()-: Godfather, you understand everything. :-()-: Tom, what we need is an Act of Congress to allow Enzo to become a citizen. :-()-: An Act of Congress! the godfather 1972
28826.0 I'm Michael Corleone--this is my father. What happened to the detectives who were guarding him? :-()-: Oh your father just had too many visitors. It interfered with the hospital service. The police came and made them all leave just ten minutes ago. But don't worry. I look in on him. :-()-: You just stand here one minute... the godfather 1972
28853.0 I have to go back to New York tomorrow. Think of your price. :-()-: You son of a bitch, you think you can brush me off like that? I made my bones when you were going out with cheerleaders. the godfather 1972
28831.0 Is it going to be all-out war, like last time? :-()-: Until the old man tells me different. :-()-: Then wait, Sonny. Talk to Pop. :-()-: Sollozzo is a dead man, I don't care what it costs. I don't care if we have to fight all the five families in New York. The Tattaglia family's going to eat dirt. I don't care if we all go down together. :-()-: That's not how Pop would have played it. :-()-: I know I'm not the man he was. But I'll tell you this and he'll tell you too. When it comes to real action, I can operate as good as anybody short range. :-()-: All right, Sonny. All right. :-()-: Christ, if I could only contact Luca. :-()-: Is it like they say? Is he that good? the godfather 1972
28737.0 I'll think about it. :-()-: Drive while you thinking; I wanna get to the City this month! the godfather 1972
28805.0 No. His name is Luca Brasi. You wouldn't like him. :-()-: Who is he? :-()-: You really want to know? :-()-: Yes. Tell me. :-()-: You like spaghetti? :-()-: You know I love spaghetti. :-()-: Then eat your spaghetti and I'll tell you a Luca Brasi story. the godfather 1972
28734.0 You look terrif on the floor! :-()-: What are you, a dance judge? Go do your job; take a walk around the neighborhood... see everything is okay. the godfather 1972
28790.0 Kay, we weren't expecting you. You should call... :-()-: I've tried calling and writing. I want to reach Michael. :-()-: Nobody knows where he is. We know he's all right, but that's all. the godfather 1972
28811.0 We have something for your mother, for Sonny, we have the tie for Fredo and Tom Hagen gets the Reynolds pen... :-()-: And what do you want for Christmas? :-()-: Just you. the godfather 1972
28850.0 Buy me out?... :-()-: The hotel, the casino. The Corleone family wants to buy you out. the godfather 1972
28813.0 Michael, what are you doing? :-()-: Shhh, you be the long distance operator. Here. :-()-: Hello...this is Long Distance. I have a call from New Hampshire. Mr. Michael Corleone. One moment please. the godfather 1972
28725.0 You filthy guinea spoiled brat. Clean it up or I'll kick your head in. :-()-: Like hell I will. the godfather 1972
28710.0 This is Tom Hagen; I'm calling for Don Corleone, at his request. :-()-: Yes, I understand I'm listening. :-()-: You owe the Don a service. He has no doubt that you will repay it. the godfather 1972
28854.0 Yeah. :-()-: Do you recognize my voice? :-()-: I think so. Detective squad? :-()-: Right. Don't say my name, just listen. Somebody shot your father outside his place fifteen minutes ago. :-()-: Is he alive? :-()-: I think so, but I can't get close enough. There's a lot of blood. I'll try to find out more. :-()-: Find out anything you can...you got a Grand coming. the godfather 1972
28731.0 Mostly it gives witnesses an excuse to change their identification when we make them see the light. Then you take a long vacation and we catch the hell. :-()-: How bad will it be? :-()-: Probably all the other families will line up against us. But, it's alright. These things have to happen once every ten years or so...gets rid of the bad blood. You gotta stop 'em at the beginning. Like they shoulda stopped Hitler at Munich, they shoulda never let him get away with that, they were just asking for big trouble... the godfather 1972
28748.0 I'm sure it's the most generous gift today. :-()-: The Senator called--apologized for not coming personally, but said you'd understand. Also, some of the Judges...they've all sent gifts. And another call from Virgil Sollozzo. the godfather 1972
28772.0 Why didn't Moe Green meet us at the airport? :-()-: He had business at the hotel, but he'll drop in for dinner. the godfather 1972
28819.0 Visiting hour ends at eight thirty. I'll just sit with him; I want to show respect. :-()-: Can I go to the hospital with you? :-()-: I don't think so. You don't want to end up on page 3 of the Daily News. :-()-: My parents don't read the Daily News. All right, if you think I shouldn't. I can't believe the things the papers are printing. I'm sure most of it's not true. :-()-: I don't think so either. I better go. :-()-: When will I see you again? :-()-: I want you to go back to New Hampshire...think things over. the godfather 1972
28828.0 I was worried when we couldn't get in touch with you in that hick town. :-()-: How's Mom? :-()-: Good. She's been through it before. Me too. You were too young to know about it. You better wait outside; there're some things you shouldn't hear. :-()-: I can help you out... :-()-: Oh no you can't, the old man'd be sore as hell if I let you get mixed up in this. :-()-: Jesus Christ, he's my father, Sonny. :-()-: Theresa. the godfather 1972
28713.0 You never think to protect yourself with real friends. You think it's enough to be an American. All right, the Police protects you, there are Courts of Law, so you don't need a friend like me. But now you come to me and say Don Corleone, you must give me justice. And you don't ask in respect or friendship. And you don't think to call me Godfather; instead you come to my house on the day my daughter is to be married and you ask me to do murder...for money. :-()-: America has been good to me... :-()-: Then take the justice from the judge, the bitter with the sweet, Bonasera. But if you come to me with your friendship, your loyalty, then your enemies become my enemies, and then, believe me, they would fear you... the godfather 1972
28823.0 Why are you so cold to her and Carlo? They live with us on the Mall now, but you never get close to them. :-()-: I'm busy. :-()-: Connie and Carlo want you to be godfather to their little boy. the godfather 1972
28739.0 You think we'll go for that last place? :-()-: Maybe, or you gotta know now. :-()-: Holy cow, I don't gotta know nothing. the godfather 1972
28722.0 What was that? :-()-: Your girl friend. She says she can't make it tonight. You lousy bastard you have the nerve to give your whores my telephone number. I'll kill you, you bastard! the godfather 1972
28755.0 Tom, I never thought you were a bad Consigliere, I thought Santino a bad Don, rest in peace. He had a good heart but he wasn't the right man to head the family when I had my misfortune. Michael has all my confidence, as you do. For reasons which you can't know, you must have no part in what will happen. :-()-: Maybe I can help. the godfather 1972
28775.0 Mike! The party starting! :-()-: Come here a minute, Fredo. the godfather 1972
28818.0 I can't... :-()-: Please say it. :-()-: Look. I'll see you tonight, OK? :-()-: OK. the godfather 1972
28765.0 That is your birthright...but Michael, use this car. :-()-: No...I would like to walk to Corleone. the godfather 1972
28799.0 What about McCluskey? :-()-: Let's say now that we have to kill McCluskey. We'll clear that up through our Newspaper contacts later. the godfather 1972
28837.0 Why don't you stop living like a bum and get this place cleaned up. :-()-: What are you, inspecting the barracks? You ready? Did Clemenza tell you be sure to drop the gun right away? :-()-: A million times. :-()-: Sollozzo and McCluskey are going to pick you up in an hour and a half on Times Square, under the big Camels sign. the godfather 1972
28796.0 Mr. Corleone is Johnny's Godfather. That is very close, a very sacred religious relationship. :-()-: Okay, but just tell him this is one favor I can't give. But he should try me again on anything else. :-()-: He never asks a second favor when he has been refused the first. Understood? :-()-: You smooth son of a bitch, let me lay it on the line for you, and your boss. Johnny Fontane never gets that movie. I don't care how many Dago, Guinea, wop Greaseball Goombahs come out of the woodwork! :-()-: I'm German-Irish. :-()-: Okay my Kraut-Mick friend, Johnny will never get that part because I hate that pinko punk and I'm going to run him out of the Movies. And I'll tell you why. He ruined one of Woltz Brothers' most valuable proteges. For five years I had this girl under training; singing lessons! Acting lessons! Dancing lessons! We spent hundreds of thousands of dollars--I was going to make her a star. I'll be even more frank, just to show you that I'm not a hard-hearted man, that it wasn't all dollars and cents. That girl was beautiful and young and innocent and she was the greatest piece of ass I've ever ad and I've had them all over the world. Then Johnny comes along with that olive oil voice and guinea charm and she runs off. She threw it all away to make me look ridiculous. A MAN IN MY POSITION CANNOT AFFORD TO BE MADE TO LOOK RIDICULOUS! the godfather 1972
28709.0 Anything...Anything the Godfather wishes. :-()-: Good. He never doubted you. :-()-: The Don himself is coming to me tonight? :-()-: Yes. the godfather 1972
28746.0 You look terrible. I want you to eat well, to rest. And spend time with your family. And then, at the end of the month, this big shot will give you the part you want. :-()-: It's too late. All the contracts have been signed, they're almost ready to shoot. :-()-: I'll make him an offer he can't refuse. the godfather 1972
28800.0 Mike, why are you cutting me out of the action? :-()-: Tom, we're going to be legitimate all the way, and you're the legal man. What could be more important than that. :-()-: I'm not talking about that. I'm talking about Rocco Lampone building a secret regime. Why does Neri report directly to you, rather than through me or a caporegime? the godfather 1972
28740.0 My business is heroin, I have poppy fields, laboratories in Narseilles and Sicily, ready to go into production. My importing methods are as safe as these things can be, about five per cent loss. The risk is nothing, the profits enormous. :-()-: Why do you come to me? Why do I deserve your generosity? :-()-: I need two million dollars in cash...more important, I need a friend who has people in high places; a friend who can guarantee that if one of my employees be arrested, they would get only light sentences. Be my friend. :-()-: What percentages for my family? :-()-: Thirty per cent. In the first year your share would be four million dollars; then it would go up. :-()-: And what is the percentage of the Tattaglia family? the godfather 1972
28742.0 No...how a man makes his living is none of my business. But this proposition of yours is too risky. All the people in my family lived well the last ten years, I won't risk that out of greed. :-()-: Are you worried about security for your million? :-()-: No. :-()-: The Tattaglias will guarantee your investment also. the godfather 1972
28830.0 Clemenza? No, I don't believe it. :-()-: You're right, kid, Clemenza is okay. It was Paulie. :-()-: How can you be sure? :-()-: On the three days Paulie was sick this month, he got calls from a payphone across from the old man's building. We got people in the phone company. Thank God it was Paulie...we'll need Clemenza bad. the godfather 1972
28711.0 Bonasera, we know each other for years, but this is the first time you come to me for help. I don't remember the last time you invited me to your house for coffee...even though our wives are friends. :-()-: What do you want of me? I'll give you anything you want, but do what I ask! :-()-: And what is that Bonasera? the godfather 1972
28712.0 No. You ask for too much. :-()-: I ask for Justice. :-()-: The Court gave you justice. :-()-: An eye for an eye! :-()-: But your daughter is still alive. :-()-: Then make them suffer as she suffers. How much shall I pay you. the godfather 1972
28848.0 Barzini wants to arrange a meeting. Says we can straighten any of our problems out. :-()-: He talked to you? :-()-: I can arrange security. the godfather 1972
28769.0 Get the car. I'll be leaving in ten minutes. Where's Calo? :-()-: Calo is having a cup of coffee in the kitchen. Is your wife coming with you? :-()-: No, she's going home to her family. She'll join me in a few weeks... the godfather 1972
28759.0 I told you that it wouldn't escape his eye. :-()-: How did you find out? the godfather 1972
28723.0 You're staying home. You're not going out. :-()-: OK, OK. You gonna make me something to eat at least? the godfather 1972
28764.0 Don Tommassino. :-()-: Michael, why must you do this. We have been lucky so far, all these months you've been here we've kept your name a secret. It is from love for your father that I've asked you never to more than an hour from the Villa. :-()-: Calo and Fabrizzio are with me; nothing will happen. :-()-: You must understand that your Father's enemies have friends in Palermo. :-()-: I know. :-()-: Where are you going? :-()-: Corleone. :-()-: There is nothing there. Not anymore. :-()-: I was told that my Grandfather was murdered on its main street; and his murderers came to kill my father there when he was twelve years old. :-()-: Long ago. Now there is nothing: the men killed each other in family vendettas...the others escaped to America. :-()-: Don Tommassino...I should see this place. the godfather 1972
28732.0 We gotta fight sometime. Let us at least recruit our regimes to full strength. :-()-: No, I don't want to give Barzini an excuse to start fighting. the godfather 1972
28749.0 He's his own boss, and very competent. :-()-: And with prison record. :-()-: Two terms; one in Italy, one in the United States. He's known to the Government as a top narcotics man. That could be a plus for us; he could never get immunity to testify. :-()-: When did he call? :-()-: This morning. :-()-: On a day like this. Consiglero, do you also have in your notes the the Turk made his living from Prostitution before the war, like the Tattaglias do now. Write that down before you forget it. The Turk will wait. the godfather 1972
28838.0 O.K. How long do you think before I can come back? :-()-: Probably a year... the godfather 1972
28779.0 The old man wants you; Johnny's here...he's got a problem. :-()-: Okay. One minute. the godfather 1972
28808.0 Tom...Tom, I'd like you to meet Kay Adams. :-()-: How do you do. :-()-: My brother, Tom Hagen. the godfather 1972
28832.0 Where are you going? :-()-: To the city. :-()-: Send some bodyguards. :-()-: I don't need them, Sonny. I'm just going to see Pop in the hospital. Also, I got other things. the godfather 1972
28708.0 Yes, I understand. I'm listening. :-()-: You owe the Don a service. In one hour, not before, perhaps later, he will be at your funeral parlor to ask for your help. Be there to greet him. If you have any objections speak now, and I'll inform him. the godfather 1972
28727.0 Clemenza. You take care of Paulie. I don't ever want to see him again. Understood? :-()-: Understood. :-()-: Okay, now you can move your men into the Mall, replace Tessio's people. Mike, tomorrow you take a couple of Clemenza's people and go to Luca's apartment and wait for him to show. That crazy bastard might be going after Sollozzo right now if he's heard the news. the godfather 1972
28743.0 I kept trying to call you after my divorce and Tom always said you were busy. When I got the Wedding invitation I knew you weren't sore at me anymore, Godfather. :-()-: Can I do something for you still? You're not too rich, or too famous that I can't help you? :-()-: I'm not rich anymore, Godfather, and...my career, I'm almost washed up... the godfather 1972
28806.0 Once upon a time, about fifteen years ago some people wanted to take over my father's olive oil business. They had Al Capone send some men in from Chicago to kill my father, and they almost did. :-()-: Al Capone! :-()-: My Father sent Luca Brasi after them. He tied the two Capone men hand and foot, and stuffed small bath towels into their mouths. Then he took an ax, and chopped one man's feet off... :-()-: Michael... :-()-: Then the legs at the knees... :-()-: Michael you're trying to scare me... :-()-: Then the thighs where they joined the torso. :-()-: Michael, I don't want to hear anymore... :-()-: Then Luca turned to the other man... :-()-: Michael, I love you. :-()-: ...who out of sheer terror had swallowed the bath towel in his mouth and suffocated. the godfather 1972
28824.0 Will you? :-()-: Let me think about it, O.K.? the godfather 1972
28756.0 Will your girl friend get back to the city all right? :-()-: Tom said he'd take care of it. the godfather 1972
28733.0 He's going to be our lawyer in Vegas. Nobody goes to him with any other business as of now, this minute. No reflection on Tom; that's the way I want it. Besides, if I ever need any advice, who's a better Consigliere than my father. :-()-: Then in a six month time we're on our own; is that it? :-()-: Maybe less... the godfather 1972
28770.0 You had better bring a few bottles home with you, my friend; you'll need help sleeping tonight. :-()-: This one could seduce the devil. A body! and eyes as big and black as olives. :-()-: I know about what you mean! :-()-: This was a beauty. Right, Calo? :-()-: Beautiful all over, eh? :-()-: And hair. Black and curly, like a doll. And such a mouth. the godfather 1972
28777.0 Mike, you sure about Moe selling. He never mentioned it to me and he loves the business. :-()-: I'll make him an offer he can't refuse. the godfather 1972
28778.0 Tom, you're the Consigliere; you can talk to the Don and advise him. :-()-: The Don has semi-retired. I'm running the Family business now. So anything you have to say, say it to me. the godfather 1972
28801.0 Bookkeepers know everything. Rocco's men are all a little too good for the jobs they're supposed to be doing. They get a little more money than the job's worth. Lampone's a good man; he's operating perfectly. :-()-: Not so perfectly if you noticed. :-()-: Mike, why am I out? :-()-: You're not a wartime Consigliere. Things may get tough with the move we're trying. :-()-: OK, but then I agree with Tessio. You're going about it all wrong; you're making the move out of weakness... Barzini's a wolf, and if he tears you apart, the other families won't come running to help the Corleones... the godfather 1972
15850.0 Hello, Peter. :-()-: You sonofabitch! :-()-: Emotional? I expected more from you. :-()-: If you kill tonight and I'm in jail the police will know I'm innocent. :-()-: By that time the game will be over. :-()-: I've figured out the message. :-()-: No you haven't. And even if you had it doesn't matter. Who would you tell? The police don't believe you, and you've just used your only phone call. knight moves 1992
15808.0 Yes you are. :-()-: Get off me! knight moves 1992
15810.0 The message! I figured out-- :-()-: Sit down! knight moves 1992
15855.0 How'd you know I wouldn't be in the same room with her? :-()-: You told me. When you called you said she was in the other room. Drop the knife. knight moves 1992
15806.0 We're interested in where you were from the time you left the auditorium until you got there. :-()-: I was at the beach. knight moves 1992
15948.0 --but you enjoy being the stronger one? You like the control. :-()-: If you're asking me if I'm passionate about what I do, the answer is yes. Without passion, nothing moves us. What's your passion? :-()-: That's a very personal question. :-()-: I see. This is going to be a very polite conversation. What shall we discuss? The weather? Movies? :-()-: Are you disappointed that I won't answer you? :-()-: I had just hoped there would be more substance to the conversation. :-()-: I thought opening too quickly was a fatal mistake in chess. :-()-: It is. :-()-: Do you always open quickly? :-()-: Are we talking about me, or chess? :-()-: You. :-()-: Each circumstance requires a different tactic. :-()-: Well, I hope you remember that tomorrow when you play Krikorian. knight moves 1992
15913.0 Excuse me... you said earlier that Mary Albert just moved in. How long ago was that? :-()-: Ten days ago. :-()-: Do you know how she found the apartment? :-()-: Through a rental agency. :-()-: Didn't you tell me that Debi Rutlege had just moved into her place also? knight moves 1992
15804.0 He's not talking about himself. :-()-: Then what's he talking about? :-()-: If we knew that we'd know the answer to the riddle, wouldn't we? He's telling us where he's going to kill tonight and we can't see it. knight moves 1992
15958.0 No. :-()-: You're not? :-()-: No -- I'm not involved. And I plan on keeping it that way. knight moves 1992
15880.0 What are you doing up? :-()-: I can't sleep. My beds lumpy. :-()-: I see. You forgot to bring you're night-light, didn't you? :-()-: That has nothing to do with it. :-()-: You want to sleep in my bed tonight? :-()-: Okay. knight moves 1992
15800.0 You don't tell us how to run our investigation. You got that? :-()-: You don't have an investigation without me. You got that? knight moves 1992
15978.0 Peter... :-()-: No, listen. You have no idea of the kind of pressure I'm under right now. :-()-: That's still no excuse. You treat everything like a game. :-()-: I can't think any more, unless it's about you. I'll be in the middle of a match and instead of thinking about my next move, I think about how you look when you smile. Remember how you said that I hide behind my chessboard? Ever since my wife died I've been... I've been afraid of getting too close to someone again -- afraid of losing them. :-()-: You're wife died. You can't feel responsible for that. :-()-: You don't understand. :-()-: Then help me to understand. I want to understand. :-()-: It's not that easy. :-()-: Of course it isn't. It's always difficult when someone you love dies. But you can't feel responsible because she had a car accident. :-()-: But I do. :-()-: Why? :-()-: Because she killed herself!! knight moves 1992
15866.0 Computers. That's how you got into Homesearchers records. You can get into anything. But why? Why? :-()-: You still haven't figured it out, have you? You think that I've put you through an ordeal. My scars run so much deeper than yours. :-()-: What scars? :-()-: The scars on your chest. From where I stabbed you with my fountain pen. knight moves 1992
15946.0 I love this hotel. I stay here every time I visit my parents. :-()-: How come you don't stay with them? :-()-: Because I love them, but they drive me crazy. You know how parents are? :-()-: No. I don't. knight moves 1992
15826.0 You should be an actor, Frank. You looked like you were really mad. The veins popping out on your neck was a really nice touch. :-()-: You weren't mad at him for picking up the phone? knight moves 1992
15979.0 This letter. I've never opened it. :-()-: Why not? :-()-: Because I know what it says. :-()-: Maybe you're afraid of what it says. knight moves 1992
15812.0 Sorry to ruin your trip to the city, but we got a real nut on our hands, Frank. :-()-: Run it down. knight moves 1992
15795.0 What do you mean, no? :-()-: He says it's a game. All games have a strategy. :-()-: All you gotta do is look at the map. :-()-: His victims aren't random. It only means that they appear to be random. There's a connection, we just have to find it. knight moves 1992
15889.0 He tapes their mouths shut. We found traces of adhesive around the victims mouths. We're doing a chemical analysis for components, but it's probably a standard brand you can buy in any hardware store. :-()-: There's only two hardware stores on the whole Island. We'll check that out. What about the blood? :-()-: Not a drop. Maybe the guy works for the Red Cross. knight moves 1992
15917.0 Why didn't you tell us you were there earlier? :-()-: I don't know. I was afraid there'd be hours of questions. I can't afford to miss a game. knight moves 1992
15975.0 He's replaying the game I played against him move by move, using these girls as the Chess pieces. All the girls have been found in their homes except Christie Eastman, who was found in back of a warehouse. Why? :-()-: Because to follow the game he played he had to move to that grid? :-()-: Right. Here! c-4. knight moves 1992
15953.0 It's going to be a long night. It could take hours before we know something. You should try to eat. :-()-: You sound like my mother. :-()-: It wasn't intentional. :-()-: Does what's going on bother you at all? Or are you just wearing your game face? knight moves 1992
15911.0 Did you see his face? :-()-: No. He was wearing a mask... but I saw the cut on his wrist. It was Peter. :-()-: I can't arrest someone for having a cut on their wrist. Do you have someone you can stay with tonight? :-()-: I've got a room at the institute I use when I stay late. :-()-: Okay. Why don't you go out there. We'll check in with you later. knight moves 1992
15935.0 You opened with the English. Lutz won't use it. He opened his first game with it. :-()-: He knows you're aware of that. :-()-: Then I'll transpose. :-()-: On a variation, yes -- but it must be a variation that is unique. :-()-: I suppose you have something in mind? knight moves 1992
15987.0 Hello, Peter. You played an interesting game last night. Even though sacrificing your Queen at b-5 is the game I played against Valsney in '82. I'm glad it helped you. :-()-: I'm sure you are. :-()-: No, I want you to do good. That way when I beat you at the end I'll look that much better. :-()-: You're getting sloppy, Yurilivich. You're nervous? :-()-: I'm not nervous. :-()-: Well, you should be, because this time I'm going to win. :-()-: Well, then this time you'll have to stay for the whole match, won't you? knight moves 1992
15793.0 What's that supposed to mean? :-()-: It means, if I were a killer and I thought the police were closing in on me I might invent someone to try and put them off the scent. :-()-: That's crazy! Why would I draw attention to myself like that? :-()-: You like to play games, don't you, Peter? :-()-: He says on the photo he'll call tomorrow at eleven. Why not come back then and listen for yourself. :-()-: Oh, we'll be back. You can bet on it. knight moves 1992
15799.0 We didn't ask for your opinion, Doctor. :-()-: Maybe you should. knight moves 1992
15927.0 As large as castles, you are still light as the air, one hundred men can't move me. It's posed as a question. What am I? :-()-: A building? :-()-: A building isn't as light as air. What's large, but as light as air and can't be moved? knight moves 1992
15883.0 Don't you have something you want to say to David? :-()-: Thanks, David. knight moves 1992
15928.0 He's not going to give us direct hints. He's going to skirt around it. :-()-: He uses castles...plural -- then says "can't move me." Singular. Not can't move us. knight moves 1992
15970.0 Did you call? :-()-: The line was busy. I'll try again. knight moves 1992
15862.0 D2-d4... b1-c3... and c1-f4. It's the number two variation of the Tarakoss opening. David, can you bring up the tournament records for the last ten years. :-()-: Mister Sanderson, they'll be hundreds of thousands of games. knight moves 1992
15955.0 I'm just curious. :-()-: I think it's best if we don't ask too many personal questions -- I want to keep things on a professional level. :-()-: You mean like in the steam room? :-()-: That's not fair! :-()-: Are you going to tell me you didn't feel something in there? knight moves 1992
15930.0 Where were you last night? :-()-: Are we going to go through this again? :-()-: Answer the question. :-()-: I went out. knight moves 1992
15920.0 What have you come up with on the riddle? :-()-: "Wee Willie Winkie runs though the street." We think he might be making a reference to himself. :-()-: Maybe, but I don't think so. I think it's just a tease. :-()-: Upstairs and downstairs in his night gown -- He could be saying the house he's picked is two stories. knight moves 1992
15859.0 What's this? :-()-: Oh, I put a few games on for your daughter. I hope you don't mind. :-()-: Of course not. knight moves 1992
15964.0 Nobody could attack you. You set your life up like one of your chessboards. You're impregnable -- but at the same time you've become trapped behind your own defenses. You're cut off from everyone around you. :-()-: What are you talking about? You don't even know me. :-()-: Does anyone? knight moves 1992
15874.0 Is your dad here? :-()-: He went down to the lobby for a minute. He should be right back. Would you like come in? knight moves 1992
15915.0 Could you tell me where you were last night? :-()-: Here. I played Gregory Lutz. :-()-: I know. You won two games against him and left the auditorium at eight forty five. Where did you go after that? :-()-: To my room and then I went for a walk. knight moves 1992
15887.0 There's no blood. Where's the fuckin' blood? :-()-: Bingo. knight moves 1992
15875.0 So, are you having a good time on the Island? :-()-: Not really. It's pretty boring. :-()-: That's only because you don't know where to go. You like hiking? Fishing? Sailing? What do you like? :-()-: Boys. knight moves 1992
15877.0 I know he really likes you. :-()-: How do you know that? Did he say something? :-()-: No... but I can tell. A woman knows these things. knight moves 1992
15905.0 Each of those grids represents almost a square mile. :-()-: That's a big area to cover. knight moves 1992
15934.0 Did you send the car? :-()-: It's over, Peter. Let's stop the games. I'm not sending a car. knight moves 1992
15937.0 We got a problem? :-()-: Our computer went on the fritz again. David came up to fix it. :-()-: Is it serious? knight moves 1992
15856.0 This isn't going to help, David. You're mother's dead. You can't undo it. :-()-: You don't understand. :-()-: Yes, I do. I found your file. I know what happened. :-()-: They took away the game because of him. My father left and my mother... There was so much blood... It covered everything. :-()-: I know, but this isn't going to bring her back. knight moves 1992
15829.0 A few people. :-()-: Did anyone stand out? :-()-: What do you mean, stand out? :-()-: Did anyone look suspicious? Think! :-()-: Now that you mention it there was somebody who looked suspicious. :-()-: What was suspicious about him? knight moves 1992
15907.0 What time did he get there? :-()-: About a quarter to one. knight moves 1992
15792.0 Casual. :-()-: Casual? You were boning her weren't you? :-()-: It wasn't serious. What's your problem? :-()-: You are! I don't like you. :-()-: Fine, don't ask me out on a date. knight moves 1992
15884.0 You ready? :-()-: For what? :-()-: You told me you'd take me over to Seattle today. :-()-: I'm sorry, honey, I can't. Not today. :-()-: But you promised. knight moves 1992
15794.0 Meaning, it looks like his victims are chosen at random. :-()-: No. knight moves 1992
15918.0 Mister Sanderson, this is Doctor Sheppard. She's a psychologist helping us out. :-()-: We've already met. Haven't we... Doctor. knight moves 1992
15982.0 You're right. The killer told me. :-()-: He didn't tell you either. knight moves 1992
15816.0 Take it easy. Both of you. :-()-: I'm sorry, this is just too convenient. knight moves 1992
15843.0 And you want these girls to feel your pain? :-()-: Please, I don't want to get into the psychological aspects of my actions. It would detract from the game. :-()-: How? :-()-: I couldn't say it any better than Huxley. knight moves 1992
15921.0 That's it? :-()-: We think he might be making a reference to drugs? Miss Emma is a street term used by junkies for Morphine. :-()-: Could Emma be the name of the girl he's going to kill? knight moves 1992
15981.0 How did you know it was "carefully"? :-()-: Frank told me. :-()-: No he didn't. knight moves 1992
15822.0 How is she? :-()-: She's unconscious, but they think she's going to make it. :-()-: You alright? :-()-: I've seen a lot of things in my time on the job, but nothing like this. Yurilivich? :-()-: I was with him the whole time until I got the call at the hotel. :-()-: Sanderson? knight moves 1992
15873.0 You must be Erica. :-()-: Uh huh. :-()-: I'm Kathy. knight moves 1992
15891.0 Same as the others? :-()-: Yeah. :-()-: How long she been dead? :-()-: I'd say at least eighteen hours. :-()-: That means she was dead before we even finished figuring out the message. knight moves 1992
15959.0 Relax. :-()-: How the hell can I relax after seeing what I just saw. :-()-: I know it was bad. :-()-: How could you? Unless you were there. knight moves 1992
15916.0 This is a printout from the hotel computer for all the messages logged to your room. Here's one at 9:04 pm. It says: From Debi. Please call me at home. :-()-: She called to give me my schedule for tomorrow. :-()-: What's interesting about it is you say you only knew her in passing. Yet, she says on her message for you to call her at home and she doesn't leave a number. That would imply you already knew the number. :-()-: I got it from the tournament directory. knight moves 1992
15963.0 What? :-()-: Have you noticed that every time we start to talk about something serious you start to play games. :-()-: I'm not playing a game now. :-()-: Yes you are. You're playing word games. :-()-: What is this? :-()-: I'm just trying to get to know you, Peter. :-()-: What? By attacking me? knight moves 1992
15852.0 That wouldn't be very sporting. Remember Huxley? "His play is always fair and just." :-()-: You're groping. I have been fair. It's my move now. :-()-: I'll give you anything you want. Anything! Please! :-()-: Don't beg, Peter. She has to die. I can't win unless she dies. knight moves 1992
15878.0 Okay. Nice meeting you. :-()-: Nice meeting you. knight moves 1992
15910.0 And what if he's wrong. If you were one hundred percent sure it was Peter you would've arrested him. If it is someone else then he's going to kill again tonight and you're sitting here ignoring the message. :-()-: We're going to work on the message. :-()-: This stinks! You want to know what I think? I think there have been five murders and you've got shit to go on. You need to blame someone and he's the easiest choice. :-()-: The most logical choice. :-()-: You don't have a shred of evidence! knight moves 1992
15789.0 I've talked to a few people who say you and her were... friendly? :-()-: Chess tournaments can be boring sometimes. People have a lot of free time. They like to gossip. knight moves 1992
15823.0 I spoke to Jeremy. He's watching Sanderson's kid. Sanderson went out after the match and hasn't come back since. :-()-: Find him! I want to talk to him. knight moves 1992
15820.0 You still having a problem with this? :-()-: Yeah, I am. I think he's playing us. If I was a killer and the police were trying to make a case against me, what better why to draw them off than to put their attention on someone else? knight moves 1992
15857.0 Don't you see? I had to make it right. I ignored my mother's crossing. I sat with them all. I held their hands. I stroked their hair. I was with them to the end. I took away the blood. I washed them. Their crossing was peaceful. :-()-: I think your mother knows that now. Why don't you put the knife down. Put it down, David. knight moves 1992
15942.0 Bainbridge Books. :-()-: Hi, Sara. This is Doctor Sheppard. I was wondering if you could tell me if you have a book on chess called "Principals and Tactics" by Anton Berger. :-()-: I can check and call you back. :-()-: Thank you. I'm at 639-7393. :-()-: We'll call you back. knight moves 1992
15865.0 What is it? :-()-: New York. 1986. Viktor Yurilivich. knight moves 1992
15954.0 I think it's interesting. :-()-: Another kind of game? :-()-: In a way. :-()-: This isn't a game. :-()-: Oh, but it is. He's killing a person everyday and challenging us to catch him. That's a game. It has rules and objectives. :-()-: How can you look at it so clinically? :-()-: Take your average cop. They deal with death everyday. If they let emotions get in the way it would cloud their judgement. :-()-: That's true -- but the emotion is still there. They just learn to control it. :-()-: What about you? Aren't there times when a young child is telling you a story so sad you just want to cry? :-()-: Of course. :-()-: Do you? :-()-: Let's change the subject. :-()-: Okay. So, tell me about yourself. Are you married? knight moves 1992
15864.0 There's three. The first is 1983. Lionel Baines. The Boston... :-()-: Never mind. He died two years ago. :-()-: 1985. Hans Korshaud. :-()-: He's in his seventies and lives in Holland. knight moves 1992
15870.0 Feeling better? :-()-: I just can't believe it. :-()-: You don't want to believe it. It's a normal reaction. :-()-: How come the police never had a record of Sanderson before. This doesn't come out of nowhere, there has to be a history. knight moves 1992
15903.0 Well, I think you have to play to his ego. He thinks he's superior. The more secure he feels, the more chances he'll take. :-()-: What did he mean by Huxley? knight moves 1992
15869.0 It worked out alright, didn't it? :-()-: Fuck off! knight moves 1992
15895.0 I see you're still having problems with your openings. :-()-: And this Jeremy-- :-()-: --Edmonds. I know. You lost to Karpov in '82, didn't you? knight moves 1992
15968.0 I'm hungry. :-()-: Call room service. knight moves 1992
15815.0 We wouldn't want someone's death to interfere with your games. :-()-: What was your relationship with her? knight moves 1992
15821.0 We've checked. There's no one with the last name of Emma on the Island. :-()-: Maybe he's going to drug her? knight moves 1992
15811.0 You want me to what? :-()-: I want you to feed every street in this grid into your computer. :-()-: It'll take hours. You can't make me do this. :-()-: You're right, I can't make you do it. Besides, you're probably gonna be too busy with the Franchise Tax Board anyway. :-()-: What are you talking about? :-()-: I gotta friend over there. He was telling me things are kind of slow. So, I figured I'd give him a call, have him come down here and look through your records. You know, give him something to do. :-()-: What's the first street? knight moves 1992
15888.0 Anything? :-()-: Not much. I don't think she was raped. There's no bruising, or any signs of trauma to the pelvic region -- and no trace of sperm which makes sense because she took a bath. :-()-: What about prints? :-()-: No prints. :-()-: You mean, no prints but hers? :-()-: No Frank. No prints. knight moves 1992
15834.0 Hello, Peter. :-()-: Who is this? :-()-: Someone who's going to become an important part of your life. I want to play a game with you. :-()-: I haven't got time for this. knight moves 1992
15861.0 Nope. Nothing. :-()-: Set up some pieces on the big board? knight moves 1992
15876.0 Do you like my dad? :-()-: Of course. knight moves 1992
15835.0 Hello, Peter. :-()-: Just a second. Hello? knight moves 1992
15798.0 What in the hell do you think you're doing? Slamming down the phone in the middle of the trace. :-()-: You think you're going to catch him on a trace? Everything he does is planned out well in advance. The only way we're going to get him is to rattle him -- make him slip up. knight moves 1992
15797.0 Are you crazy? :-()-: Let's see how much he wants to play. knight moves 1992
15813.0 Debi Rutlege. Female. Caucasian. Twenty four. Worked over at the Four Oaks Hotel. :-()-: Local? :-()-: No. She just moved here last month from Portland. knight moves 1992
15844.0 Huxley's quote also says, "his play is always fair and just." :-()-: So is mine, within the framework of my rules. knight moves 1992
15961.0 Can you go fifteen minutes without thinking about it? :-()-: No. But I'm open to distractions. :-()-: I'm sure you are. knight moves 1992
15831.0 Don't fuck with us! Where did you go! :-()-: He was with me? knight moves 1992
15851.0 Yes? :-()-: Congratulations on your daring escape. You just missed me by a few seconds. It's check, Peter. :-()-: Let me speak to my daughter. :-()-: She's in the other room. I just wanted you to know she wasn't dead... yet. But it's time for her to die now. :-()-: Please... wait. :-()-: The game's over. You lost. :-()-: It's me you want. Not her. :-()-: No. As usual you're wrong. It is her I want. Killing you would be easy. Living with the consequences of losing will be much more of a defeat. knight moves 1992
15814.0 What do you think? :-()-: He's got an answer for everything, but he doesn't have an alibi. :-()-: You think he's dirty? :-()-: I don't know, but I think you're right. He's lying. knight moves 1992
15962.0 You know, you're not the easiest person in the world to get close to. :-()-: You always want to talk about me. What about you? :-()-: Wasn't I just talking about me? :-()-: No. You were talking about chess. :-()-: Alright. What about me? :-()-: I dunno. Where's Erica mother? :-()-: She died in a car accident. :-()-: I'm sorry. :-()-: It's alright. It was a long time ago. :-()-: It must've been very hard on Erica? :-()-: It was. :-()-: And you? knight moves 1992
15790.0 By yourself? :-()-: Yes. knight moves 1992
15983.0 This isn't going to be like the phone book, is it? :-()-: Of course not. knight moves 1992
15899.0 I don't think it matters. Last nights victim, Christie Eastman was found in a warehouse on the outskirts of town. The night before, Debi Rutlege was found in the center. :-()-: Meaning? knight moves 1992
15846.0 What? :-()-: You heard me. Why did you go out to the institute looking for her? :-()-: What makes you think I was there? :-()-: I couldn't tell you that. It would ruin the game. :-()-: The game's almost over, Peter, and you're running out of time. knight moves 1992
15796.0 Morpheus. :-()-: What? :-()-: Not what -- who. Morpheus. The Greek God of dreams. Look at the next line. "Her God has gone and left his home." knight moves 1992
15929.0 A shadow. What's as big a castle? As light as air , but one hundred men can't move it? The shadow of the castle. Are there any buildings that have the name Castle in it that cast a shadow long enough to fall across another building? :-()-: Wait a minute. There's an apartment in that area called the Castle Arms. :-()-: That's got to be it. knight moves 1992
15965.0 I just don't want to fight with you. :-()-: Then show me who you are. knight moves 1992
15906.0 As large as castles. :-()-: Yes. Why not just say a building if he meant any general type of structure? knight moves 1992
15881.0 Who told you that? :-()-: Mrs. Lutz. She also told me that Mr. Lutz goes to a medium to try and contact great Grandmasters in the spirit world. :-()-: Has he gotten through to any? :-()-: No, but he did contact a dead parcheesi champion from Ohio. knight moves 1992
15938.0 Peter... :-()-: I've got be here for the police when they come. Then I've got to practise. In case you forgot I'm in the middle of a match right now. :-()-: You're always in the middle of a match. :-()-: I want to be the best I can. knight moves 1992
15809.0 You set yourself up tonight when you attacked Kathy, you crazy fuck! :-()-: What? knight moves 1992
15830.0 It doesn't make sense because we don't understand it. :-()-: But a hundred men could move him. knight moves 1992
15838.0 I did another one last night. You might have saved her, but you didn't want to play. :-()-: Where is she? :-()-: You'll find her. :-()-: If you got something to say to me, just come out and say it! knight moves 1992
15890.0 This is where he forced his way in. We found some fibers on the windowsill, probably particles of clothing that rubbed off when he climbed through. I'll know more once I get it under the microscope. :-()-: That's great. :-()-: Wait, I've saved the best for last. Andy, hit the lights, will you? knight moves 1992
15984.0 You're going to be late for your match. :-()-: Are you going to come tonight? :-()-: Yeah. I'll be there later. knight moves 1992
15801.0 You know, I figure that's pretty much how these girl's feel just before they get it. You think I'm right? :-()-: I wouldn't know. :-()-: That's right. I guess only the killer would know that. :-()-: How'd you get in here? :-()-: The door was open. :-()-: No it wasn't. :-()-: Of course it was. Otherwise I'd be breaking and entering. That's a felony. :-()-: What do you want? :-()-: You ever do any hunting, Peter? knight moves 1992
15904.0 Maybe we should back up a minute and take a look at what we do have. All the girls that have been killed have been killed at night. They're all the same type. All moved into their homes within the last three months -- and all of them found their homes through Homesearchers. :-()-: I thought that Homesearchers was a dead end? :-()-: It can't be a coincidence. The woman that owns it has a son. She says he's been on vacation in Montana for the last ten days. We're trying to locate him. There's also a cleaning service that comes in once a week. We're checking that out too. :-()-: He moves around a lot. Why? knight moves 1992
15828.0 Sending yourself anonymous notes in the mail is one thing -- but who called him today? :-()-: I don't know. Maybe there's two of them. Maybe he hired some Wino to make the calls. :-()-: Maybe you're reaching a bit? I think Sanderson should be in on this. There's a reason why the killer's calling him. knight moves 1992
15966.0 This frightens me -- because I'm starting to feel things I haven't felt in a long time. :-()-: You've got to face the things you feel. :-()-: I thought you were the woman who didn't want to get involved. :-()-: I said I didn't plan on getting involved. knight moves 1992
15868.0 The police came to me for help. What could I do? :-()-: You could have been honest with me for starters. We work together. I have to be able to trust you. :-()-: I admit I handled it badly. Sanderson wasn't going to talk to me... but you're young, attractive-- :-()-: --the same type as the girl who got killed. Jesus, Alan, the guy could be a psychopath. :-()-: They asked who would be best suited for this and you-- :-()-: Don't patronize me! You sent me into a potentially dangerous situation and didn't warn me! knight moves 1992
15936.0 It's a good play, but risky. :-()-: You don't win by playing it safe, Peter. knight moves 1992
15898.0 That would indicate he's playing out a fantasy. Power-control killers usually fantasize about their actions long before they commit them. Once they become a reality though, they reach a sense of euphoria and need to repeat the act to sustain it. But, in all the research I've read on Serial Killers, I've never heard of one moving so fast. It's as if the game is the catalyst for the murders -- not the other way around. :-()-: Anything else? :-()-: Yes. Why disguise your voice if no one knows it? :-()-: I was thinking the same thing. He must be local. It's logical for him to assume that the police would be there, but he recognized Andy's voice and called him by name. What about this? :-()-: I would think it's some type of clue as to where he's going to kill next? knight moves 1992
15886.0 Whaddaya got, Nolan? :-()-: Not much. There's no sign of a break in. I think she let him in. :-()-: How long has she been dead? :-()-: Six, eight hours tops. :-()-: Any sign of rape? :-()-: Not that I can see. We'll know more once I get the lab reports back from Seattle. knight moves 1992
15848.0 Hello. :-()-: Last night was very exciting, wasn't it? Have you figured out what I'm doing? :-()-: You're playing the Tarakoss opening. :-()-: Very good. :-()-: You're next move should have been e2- e3. :-()-: I used a variation. You should have anticipated that. Have you figured out the message? :-()-: What word did you leave last night? :-()-: The police haven't told you? :-()-: They think that you and I are doing this together. knight moves 1992
15893.0 Only that you wanted someone from the institute to talk to Sanderson for some case you're working on. What'd he do? :-()-: We're not sure. :-()-: What do you think he did? :-()-: We think he could be a suspect in a murder investigation. knight moves 1992
15943.0 Bainbridge Books. :-()-: Sara, this is Doctor Sheppard. I called you earlier about a book. :-()-: Yes, Doctor Sheppard. We called you back but you weren't home. We have the book. :-()-: Could you please do me a big favor? In the first chapter the author mentions his three rules of chess. Could you look and tell me what they are? :-()-: Of course. Yes, here it is. Why, they're all the same? :-()-: What are they, please? :-()-: "Carefully". "Carefully". "Carefully". knight moves 1992
15956.0 You're projecting your own desires and reading more into what happened than what actually did. :-()-: Very good. Spoken like a true psychologist. When confronted with the prospect of your own reality, hide behind quotations. What is that Masters and Johnson? knight moves 1992
15908.0 He's right, Kathy. :-()-: What are you saying? That I'm seeing what I want to see? That I'm protecting a murderer? What? knight moves 1992
15960.0 I know you didn't :-()-: You know, Andy thinks you're doing this. :-()-: Doesn't that scare you? :-()-: No. :-()-: Why? :-()-: Because I think he's wrong. knight moves 1992
15973.0 Peter... :-()-: How could you think that, Kathy? How could you even consider it? knight moves 1992
15842.0 Just for that no hint today. :-()-: Why are you doing this? You must have some idea of the pain you're causing people. :-()-: Pain? Pain is just a state of mind. It's something you learn to live with. I have. knight moves 1992
15952.0 You sure you don't want to eat something? :-()-: I don't think I could. knight moves 1992
15980.0 If we could just figure out what the next word is going to be. He said the game's going to end tonight so there's only going to be one more word. :-()-: It could be anything. :-()-: It's got to be grammatically correct. knight moves 1992
15827.0 Why does he call Sanderson? :-()-: He's one of the best Chess players in the world. Who better to play a game with? knight moves 1992
15802.0 No. :-()-: I used to. With my old man. He taught me how to hunt and trap. Trapping's a lot harder than most people think. We used to go after Raccoons mostly. They'd get into our garbage, our fields. When an animal can't live peacefully with those around it, it has to be destroyed. But they're crafty little devils. You see the trick is, you can put your trap down, but no Raccoon is going come near it unless you lay down the scent. You ever smell Raccoon scent? Smells like shit, but to a male Raccoon it smells just like pussy. He'll walk right up to that trap, even though it don't look nothing like a Raccoon and stick his Goddam head right in it. You know why? Cause he can't help himself. The scent drives him. So, if you want to catch a Raccoon all you gotta do is figure out where he is -- lay down the scent -- and sooner or later he'll walk right into the trap. :-()-: What if he doesn't bite? What if he's an exceptionally bright Raccoon? :-()-: Well then, you just gotta find out where he is -- and once you're sure where he is -- you shoot the fucker. knight moves 1992
15945.0 Is this your first time here? :-()-: Yes. knight moves 1992
15985.0 You beat him. :-()-: I got lucky. :-()-: There's a girl in the hospital who is going to live because of you. You made a difference. knight moves 1992
15971.0 They're not delivering. I'm going to go pick up the pizza. :-()-: I thought you said the line was busy? :-()-: I tried again and got through. :-()-: What's wrong? :-()-: Nothing. :-()-: Then why are you backing up? knight moves 1992
15986.0 It's over now. :-()-: I just wanted you to know in case you thought about it in the future. :-()-: Do we have a future? knight moves 1992
15836.0 Very amateurish, Peter. I'm surprised you would use such an obvious tactic. I'm not an idiot! Don't treat me as one! I'll call you everyday... You get one minute, whether you put me on hold or talk is up to you! Are you ready to play? :-()-: Yes. Why did you kill Debi Rutlege? :-()-: To get your attention. :-()-: Did you know her? :-()-: No, only the path she chose to travel. knight moves 1992
15845.0 Have you any idea what the message is? :-()-: If it's so important, why don't you just tell me? :-()-: I couldn't tell you that. It would ruin the game. Not that you're playing it very well. :-()-: You like to brag, don't you? knight moves 1992
15860.0 Sure. We've got a modem line hooked up with the data base in New York. :-()-: Can we correlate data? Look for specific things, like player's ages... stuff like that? :-()-: No problem. We're in. :-()-: Okay. Run a search to see if there are any players with a rating above two thousand that live on the Island. knight moves 1992
15901.0 Before we go in I gotta tell you this isn't going to be pretty. :-()-: I know that. I've seen the photo's. :-()-: This ain't no photo. knight moves 1992
15925.0 I still don't see how he could be playing chess against you. There's only enough grids on the map to be half a board. :-()-: That's all he needs. He's making the moves. It's an opening. I just can't remember the name. Okay, it's a game. He's starting it, so he moves first. He's white. e2-e4. knight moves 1992
15944.0 Sorry. I thought it was empty. :-()-: It's alright. knight moves 1992
15803.0 You got it on the board. :-()-: No, I need the original note. :-()-: There's a photostat of the original on the wall. knight moves 1992
15885.0 It isn't Kathy. :-()-: Who's Kathy? knight moves 1992
15892.0 Well? :-()-: Well what? I told you this was a stupid idea. You can't learn anything from someone in a few minutes. :-()-: You didn't pick up any vibes from the guy? :-()-: I'm a psychologist, Frank... not a psychic. What's this all about anyway? :-()-: What did Dr. Fulton tell you? knight moves 1992
15819.0 The F.B.I. has nothing remotely similar to this guy. I think he's a first- timer. :-()-: Check with the State. If he's never killed outside of Washington the F.B.I. wouldn't have it. Nolan? knight moves 1992
15967.0 You can turn everything around so easily. This is not just another game, is it? :-()-: No. knight moves 1992
15926.0 It's a commercial area. No one lives there. :-()-: But he had to dump the body there to make the move. knight moves 1992
15949.0 Well, I think I've had enough. :-()-: Getting too hot in here for you? knight moves 1992
15923.0 As large as castles. You are still light as air, one hundred men can't move me. :-()-: It doesn't make sense. knight moves 1992
15900.0 His home would be... Mount Olympus. :-()-: Call dispatch. Double the patrols. I want that area blanketed. knight moves 1992
15932.0 Don't you understand what I'm telling you? That's why he wants me out of the way. :-()-: I understand that this is just another one of your games. :-()-: Look, I'll do anything you want! I'll sign anything you want! Just send a car out to the hotel. knight moves 1992
15837.0 And -- what path is that? :-()-: We all have paths to follow. You hope yours will lead you to me. :-()-: Why did you write remember on the wall? :-()-: That's something you'll have to figure out for yourself. Really, Peter, you can't expect me to answer such direct questions. :-()-: Why not? :-()-: You don't want to think and that's why I'll win! I'm already two points ahead. :-()-: What? knight moves 1992
15924.0 He's using the map as a chessboard! The sonofabitch is playing chess with me!! :-()-: How can he be playing Chess with you? You're not making any moves against him. :-()-: Maybe I already have. knight moves 1992
15914.0 I know you're busy so I'll get right to it. Did you know her? :-()-: Only in passing. To say hello to. knight moves 1992
15840.0 Hello? :-()-: Do you know what I'm doing right now, Peter? I'm looking at the name of the girl I'm going to kill tonight. :-()-: You know her? :-()-: Not really. :-()-: Why her? :-()-: Because she's the type. :-()-: But you said you didn't know her. :-()-: I know what I said! She looks just like... :-()-: Just like who? :-()-: I really wish you'd stop trying to maneuver me. I find it irritating, not to mention insulting. :-()-: I'm just trying to play the game. :-()-: You're not playing very well. There are clues all around you and you keep missing them. knight moves 1992
15912.0 If you can think of anything else give us a call. :-()-: You know, I hate to say I told you so, but I warned her. Coming home at all hours of the night. A young girl new to the city. This neighborhood ain't what it used to be. knight moves 1992
15931.0 Jeremy would have never killed himself. :-()-: Maybe he just got tired of covering for you. :-()-: This is insane! Can't you see what's going on. The killer wants me out of the way. He's setting me up. knight moves 1992
15825.0 Ready for round two? :-()-: I've got a feeling we're going to go the distance with him. Let him sweat for a little while. knight moves 1992
15957.0 You have to win every point, don't you? :-()-: I just wanted to know if you're involved with anyone? Let me ask you something? Do your colleagues know if you're involved or not? :-()-: Of course. :-()-: Why? :-()-: Because I work with them. knight moves 1992
15969.0 Fine. :-()-: What do you want on it? :-()-: You. :-()-: Would you settle for pepperoni? :-()-: If I have to. I'm going to take a shower. knight moves 1992
15871.0 Kathy, please. You're going to wear a hole in the carpet. :-()-: I'm just nervous. Sorry. :-()-: You're safe. He wouldn't come here. :-()-: He already did once. knight moves 1992
15872.0 Why did he? :-()-: Why did he what? :-()-: Come here. :-()-: He was watching you. :-()-: Yeah -- that's what we've always thought -- but what if he wasn't? What if I had nothing to do with the reason he came here? :-()-: You're losing me. knight moves 1992
15922.0 Another one? What word did he leave? :-()-: "Is". Did you tell him about the institute? knight moves 1992
15833.0 We'll find the evidence. :-()-: You couldn't find your dick in a wind storm! knight moves 1992
15853.0 I was thinking about leaving the phone line open so you could hear her die -- but I've decided it's better if you don't know exactly when I kill her. Good-bye Peter. :-()-: No! Wait!! knight moves 1992
15879.0 Congratulations, dad. :-()-: My cheering section. knight moves 1992
15817.0 No shit! :-()-: Could someone this disturbed give the appearance of being normal? knight moves 1992
15933.0 Alright, c'mon Peter. We're moving you to a cell. :-()-: I signed the confession. What about the car? knight moves 1992
15972.0 Peter, you have to admit-- :-()-: --admit what? That I was right about you in the steam room? That you're willing to do anything to find out what you want? Would you like me to leave so you can search the rest of the room? knight moves 1992
15841.0 I'm sure there are other people who would be interested in what I have to say. :-()-: Then call them! knight moves 1992
15791.0 We seem to have a little problem here? :-()-: What kind of problem? :-()-: I think you're lying. That's what kind of problem. :-()-: What are you saying? knight moves 1992
15807.0 You sure you weren't over on Pine Road? :-()-: I'm positive. :-()-: You're a lying bastard! :-()-: Fuck you! I'm tired of your goddam accusations! If you want to arrest me go ahead! Otherwise back off. :-()-: It's all a big game isn't it? A sick, fuckin' game. I don't know if there's two of you involved, or what -- but we're going to find out. Last night you gave us just enough to send us to the wrong place. It must've shocked the hell out to you when Frank walked in on Pine Road and you couldn't finish. knight moves 1992
15974.0 In a fierce magazine you'll find a hint of my actions to come... Why does he set this line apart? :-()-: For emphasis? :-()-: Exactly. It's the key. :-()-: But what does he mean by a fierce magazine? Violent? :-()-: A fierce magazine... brutal... An angry magazine... A war magazine... a mercenary magazine... fierce... Mad. :-()-: What? :-()-: Where's the note? knight moves 1992
15909.0 We can't rely on your judgement anymore. :-()-: What does that mean? knight moves 1992
15863.0 Now what? :-()-: Have computer search for anyone that's used that opening against me. There can't be more than a handful. knight moves 1992
15902.0 You never know how you're going to catch a suspect. :-()-: Peter's right, Captain. He's got to be forced into making a mistake. knight moves 1992
15854.0 I know where you are. :-()-: Very good. How did you know? :-()-: The pipes. knight moves 1992
15805.0 Then let's narrow it down. :-()-: Homesearchers. knight moves 1992
15818.0 Sorry. :-()-: Anything? knight moves 1992
15950.0 What are you looking for? :-()-: Nothing. I just came in for a steam. :-()-: No you didn't. knight moves 1992
15858.0 Naw, it's all fixed. I also loaded up a program that'll analyze your games three hundred percent faster. :-()-: Thanks. knight moves 1992
15919.0 You want me to go with you? Maybe I can help. :-()-: That's alright. We'll take it from here. knight moves 1992
15882.0 What is it, dad? :-()-: Nothing. knight moves 1992
15824.0 What is it? :-()-: It's a voice modulator. knight moves 1992
15832.0 Remember eventually revenge is carefully... Have you tired juxtaposing the words? :-()-: Oh, c'mon. We're not going to spend any more time on this crap, are we? It doesn't mean anything. It's Sanderson! :-()-: It isn't him. Frank, you brought me in on this in the beginning because you wanted my opinion if he was capable of doing this. :-()-: Jesus, you're sleeping with the guy. You've lost your perspective. You can't possibly be unbiased. knight moves 1992
15941.0 She went into town with Mrs. Lutz. Did they get him? :-()-: No. :-()-: Feel like practicing? :-()-: Yeah. Just let me grab a shower. knight moves 1992
15867.0 You might not have done it had you known. :-()-: You're damn right! I'm a child psychologist and you send me into a room with someone who could be a murderer! :-()-: Would you mind keeping your voice down? I have guests. :-()-: Oh, well we wouldn't want to disturb your guests, would we? knight moves 1992
15897.0 Kathy? :-()-: Well, the fact that he views this as a game suggests that he is trying to prove some sense of superiority -- and the way he murders confirms a need to be in complete control of his victim. :-()-: What about the way he arranged the body? knight moves 1992
15976.0 Hi. :-()-: Can I come in? knight moves 1992
15977.0 The other night -- I said some things that maybe I shouldn't have. I mean, you haven't known me very long and I can see how you thought what you did. :-()-: I'm not looking for an apology, Peter. :-()-: I was thinking that... maybe when this is all over we could... :-()-: I want someone I can get close to. I don't know if that's possible with you. :-()-: You really haven't seen my best side. knight moves 1992
15847.0 It's you who's running out of time. You're starting to make mistakes now. You're wondering just how much I really know. Just how close I'm getting? Well, I'm closer than you think, pal -- and I'm gonna nail your ass to the wall! :-()-: Very nice speech, Peter. Did you rehearse that, or was it impromptu? There's an old wooden bench in the garden. Next to it is a rock. You'll find a message for you under it. Let's see if you're as clever as you think you are. knight moves 1992
15896.0 No. I told him to. I wanted to make sure there was someone on the other end. :-()-: But we heard him. :-()-: It could have been a tape. A prerecorded conversation between Sanderson and himself. knight moves 1992
15849.0 Interesting concept. I hadn't thought of that. If you think about how Anton Berger plays chess you might get it. :-()-: I'm beginning to think it doesn't mean anything. Remember eventually revenge- - :-()-: --you're hopeless! You can't even read a sentence! Didn't they teach you punctuation in school? The game ends tonight! knight moves 1992
15894.0 I'm busy right now. :-()-: It's important. knight moves 1992
15939.0 You have something you want to say? :-()-: Just thought you might want to talk. :-()-: About what? :-()-: Whatever's on your mind. :-()-: Who says something's on my mind. knight moves 1992
15839.0 I suppose you want to know where I'm going to kill tonight, Peter? :-()-: You're not going to tell me that. :-()-: "Wee Willie Winkie runs through the town. Upstairs and downstairs in his nightgown. Crawling through the window. At the end of Miss Emma's street. Her God has gone and left his home. So her and I can meet." knight moves 1992
15947.0 Are you with the tournament? :-()-: Uh huh. :-()-: Are you one of the players? :-()-: Yes. :-()-: I've always wanted to learn how to play chess. I don't have the patience for it. When did you start playing? :-()-: When I was very young. :-()-: It seems like such a complicated game. :-()-: Not really. You see your goal and you go after it. Anything that gets in the way is an obstacle and must be destroyed. :-()-: Sounds very violent. :-()-: Chess is a reflection of life. Life is violent. The strong win. The weak perish. knight moves 1992
15951.0 They couldn't break the riddle. :-()-: Did you think it was going to be easy? You think he was going to lay it out at your feet? :-()-: We need your help. :-()-: I offered my help this morning and Sedman turned me down. :-()-: But you're the key. The one he wants to play the game with. :-()-: I can't right now. I've got a game. :-()-: She could be dead after the game. :-()-: She could be dead now. :-()-: What if she isn't? Where are your priorities? You have to think between someone's life and a chess game? The girl he killed last night was only twenty one years old! He dumped her body behind a warehouse like a sack of garbage! knight moves 1992
15940.0 What do you see here? :-()-: Mate in five. :-()-: Exactly... and you did this. knight moves 1992
17313.0 Had enough? :-()-: Even the alarm didn't wake him. logan's run 1976
17358.0 I hate outside! I hate it! :-()-: We'll be all right... We will... logan's run 1976
17304.0 Answer the question! :-()-: Do you know how long all this will last? Not thirty years... or thirty thousand years... but thirty thousand years... and you'll be part of it. Ages will roll... Ages. And you'll be here... the two of you... eternally frozen... frozen... beautiful. :-()-: There must be somebody else up here. I can't believe that he's -- :-()-: Let me sculpt you and I will show you where the others have gone. :-()-: That's better. How do you want us? :-()-: Nude. Imagine, a pair. :-()-: It'll be all right... logan's run 1976
17325.0 I just might look in on New You 483 myself. :-()-: You? Why? You're already beautiful. :-()-: No -- it's that last Runner -- someone in 483 was trying to help him. logan's run 1976
17374.0 "Beloved son"... So people stayed together for that feeling of love... They would live and raise children together and be remembered. I think I feel that way, Logan. Can we be that way? :-()-: Yes. You and I, Jessica. :-()-: And Sanctuary? :-()-: Sanctuary is the right to live... nothing more. But nothing less, either... logan's run 1976
17340.0 Why show me? :-()-: I'm going to run. :-()-: Why tell me? :-()-: You know something. :-()-: About running, dying what? :-()-: Both... running's what I'm interested in. :-()-: I know what everyone knows. Try like hell for Renewal. You have the same chance everyone else has. :-()-: It's different now. Help me. :-()-: How can I? logan's run 1976
17365.0 What does it mean? :-()-: The Lifeclocks have no power outside. logan's run 1976
17341.0 Where did you get that? :-()-: A Runner gave it to me. :-()-: And then you killed him, right? :-()-: I let him go... believe me. :-()-: I don't.. :-()-: Speak to your friends for me, Jessica... please... :-()-: Please? What friends? :-()-: I don't have much time. :-()-: I never heard of a Sandman running... ever... :-()-: And I never heard of Sanctuary. logan's run 1976
17352.0 Will you take me with you? :-()-: Why, Jessica? You're still a green. logan's run 1976
17345.0 A Runner... Cathedral. A woman. :-()-: You're not going, are you? :-()-: Why not? Maybe she'll help me. You won't. You'd better stay here. logan's run 1976
17301.0 Where do you think we came from? :-()-: From? From? From? :-()-: We were sent here and you know it. Others have been sent here. Where are they? Hiding? logan's run 1976
17359.0 Don't! :-()-: Sooner or later, we'll have to try something. logan's run 1976
17333.0 Please... no? You mean "not here" -- that's it? You're a private Available but particular. Don't worry. There's no one here but me. And you. :-()-: No. Just no. :-()-: You prefer women? :-()-: No. :-()-: Well then...? :-()-: Nothing. I felt sad, I put myself on the circuit. It was a mistake. :-()-: Sad? What made you sad? :-()-: A friend of mine went on Carousel tonight. Now he's gone. :-()-: Yes... probably he was renewed? :-()-: He was killed. :-()-: Killed? Why do you use that word? :-()-: Isn't it right? Isn't that what you do? Kill. :-()-: I never 'killed' anybody in my life. Sandmen terminate Runners. Who brought you? :-()-: Nobody. I felt sad... I put myself on the circuit. :-()-: You felt sad. What's your name? :-()-: Jessica. :-()-: You're beautiful. Let's have sex. :-()-: No. :-()-: Later. :-()-: No. :-()-: But you put yourself on the circuit! :-()-: I thought I had to do something. :-()-: And? :-()-: I changed my mind. :-()-: And now? :-()-: Curious. :-()-: About what? :-()-: How a Sandman lives. logan's run 1976
17296.0 For me? Better feel sorry for yourself, Sandman! :-()-: No, for you! How old are you, Billy? logan's run 1976
17334.0 Let's have sex. I thought you were curious. :-()-: Not about that. :-()-: I'm listening. :-()-: I'm afraid to tell you. :-()-: I'm not armed. Well? :-()-: Why is it wrong to run? :-()-: You shouldn't even think such things... And you picked a strange person to say them to -- :-()-: I suppose. But what if you want to live? :-()-: So? Do what everyone does. Try like hell for renewal. logan's run 1976
17379.0 What kind of jewel is this? :-()-: I don't know. :-()-: You're both full of secrets like Macavity. Did you steal this? :-()-: No. :-()-: "Macavity, Macavity, there's no one like Macavity, There never was a cat of such deceitfulness and suavity." logan's run 1976
17405.0 Well? How do you like it? :-()-: I don't know. The cheeks maybe... look a little -- :-()-: Cheeks? Cheeks? Right. Too much, you think? :-()-: Too little. :-()-: Too little? Too little. Okay, wait for me. logan's run 1976
17361.0 Do you think everything's going to turn to ice? :-()-: I doubt it. :-()-: Don't ever let go. :-()-: I won't. logan's run 1976
17397.0 To a city with thousands and thousands of people. :-()-: Alive? logan's run 1976
17369.0 Look at his face... and his hair... Is that what it is to grow old? :-()-: It could be... logan's run 1976
17354.0 What do you suppose this was...? :-()-: Some kind of breeding pens... I suppose... They say people used to breed animals, fish, anything... ...to eat, of course. :-()-: Ycch. To kill things and then eat them. It must have been a savage world. logan's run 1976
17370.0 That sweet madman -- how could he come to exist? :-()-: He had a mother and father -- and he knew them. :-()-: One in a million, I suppose logan's run 1976
17322.0 Now there's a few who could have been his seed-mother. :-()-: Only a few? You're just not trying. logan's run 1976
17344.0 If you did know, you'd tell me. :-()-: Of course -- :-()-: If you trusted me, you'd know. :-()-: We're coming to Arcade. Shall we Relive together? logan's run 1976
17363.0 Maybe we're the first ones to get through... Maybe Sanctuary is near, now... another protected place. It couldn't be outside. :-()-: How would anyone know? Even if we find it -- we can never go back. logan's run 1976
17367.0 I have never seen a face like that before. It must be the look of great age. Whoever he was he was terribly old. :-()-: Yes, do you think that's why he looks so sad? logan's run 1976
17401.0 I know... We're going to try and get in this way. I don't think you can make it. :-()-: Oh... I did so look forward to seeing all those people. :-()-: I'm sorry. :-()-: Yes... :-()-: Can you make it back? :-()-: Oh my... I'll try. logan's run 1976
17337.0 You could have called me yourself. :-()-: But I wasn't sure you'd come. :-()-: Here I am. Shall I come in? logan's run 1976
17387.0 We don't get many Sandmen. I think we've only had one other since I've been here. :-()-: A Sandman can get as sick of his face as anyone else. Where's the doctor? :-()-: I like your face. Would you mind if Doc took a picture? I'd like him to give your face to somebody else. :-()-: It's all right with me. Is he here? :-()-: My name's Holly... Holly 13. In ancient times they said my number was unlucky. Do you believe in luck? :-()-: No -- Look, I'm in a hurry. logan's run 1976
17349.0 I'm ashamed. I was bringing you to be killed. :-()-: Where? Sanctuary? Can you take me there? :-()-: Logan, I don't know where Sanctuary is. But if I take you to them, they'll kill you. :-()-: All right. But why? I didn't kill the Runner. :-()-: Yes, but they won't know that... or care. They're hunting you, Logan. Maybe me too, now... :-()-: That's nothing... there's a Sandman behind us, too and there'll be more soon. Take me to them. :-()-: I -- I can't. :-()-: Then -- why don't you leave me -- go to them -- explain :-()-: No. Not that either. logan's run 1976
17338.0 I couldn't get you out of my mind. :-()-: I'm the most beautiful woman you've ever seen, I suppose? :-()-: Maybe... sure... :-()-: Thanks... but I have the choice. :-()-: Of course. :-()-: Then it's still no. logan's run 1976
17329.0 It's him! The first Sandman. He killed... Doc. :-()-: No, Holly -- wait! He's running. Tell them the rest! :-()-: He's the one. You too. I remember. He was in a hurry. Just a face job. Dark hair, I said. Then he killed Doc and you grabbed me -- and the machine blew up and I ran... I ran. :-()-: Holly. Holly! Please... The other Sandman. Remember the one who came after -- logan's run 1976
17402.0 Break-in scanners report intrusion, identify. :-()-: Logan-5... Francis-7, authorized duty quadrant. Intrusion accidental. :-()-: Clear Logan-5 and Francis-7. logan's run 1976
17308.0 It's a real privilege, Sandman. :-()-: Thanks. I thought you'd be older. I expected a Red. :-()-: I am. :-()-: Your own work? :-()-: And I did it myself right on there. logan's run 1976
17396.0 We're leaving. :-()-: What a pity. I was hoping you'd be here to bury me. logan's run 1976
17406.0 No -- just there -- on the first level. Don't look for us. We'll see you. :-()-: You don't seem quite sure, Jessica. Can you do it? Will you? logan's run 1976
17295.0 I'm a Sandman! :-()-: I cut up a Sandman yesterday. They said I'd never get him... but I cut him up good, I did. :-()-: I feel sorry for you, boy! logan's run 1976
17302.0 Hiding? Yes! Hiding, hiding. :-()-: Where do we go?! Where do we go from here??!! logan's run 1976
17399.0 Is that really it? It doesn't seem very far. Will we be there soon? :-()-: I promise. We'll go on as soon as it's light. logan's run 1976
17357.0 What's that? :-()-: It feels like breath. It makes everything move. Your hair is moving. :-()-: And yours. logan's run 1976
17377.0 Yes, cats, of course. What else could they be? Cats. Of course each one has his own name too. :-()-: But there are so many of them. Do you know each one separately. :-()-: Yes indeed, everyone. Actually, they all have three. "The naming of cats is a difficult matter. It isn't just one of your holiday games. You may think at first I'm mad as a hatter when I tell you a cat must have THREE DIFFERENT NAMES." An ordinary name and a fancy name. That's two. Do you want to guess what the third one is? logan's run 1976
17381.0 We'll have to bury him. :-()-: What's that? :-()-: They're put into the ground so they can be visited by the living... logan's run 1976
17373.0 Jessica... listen to me... listen to me... The Lifeclocks made me kill Francis. They make people die or be killed every day. If I didn't try and destroy that... I couldn't live here or anywhere. Do you understand? :-()-: I want to be alive and with you, that's all I want. logan's run 1976
17393.0 What's beyond this place -- do you know? :-()-: No, no, no :-()-: Did your Mother or Father ever mention another place? :-()-: Never, never, ever. Nothing. logan's run 1976
17366.0 You can have any woman in the city. What do you really want? :-()-: You know, Jessica. :-()-: ...But I still have the choice...? :-()-: Of course. :-()-: Then the answer's yes... logan's run 1976
17330.0 That's right. The other one came after. The older one. Smashing, killing, burning! :-()-: ...and he was hunting the first one, this one. Wasn't he? Wasn't he? This one was running, the other one was hunting him... :-()-: Yes. Oh yes. He was after you. I remember. You're running! logan's run 1976
17360.0 It's getting dark and cold. I'm tired. :-()-: Why don't we rest here? We know we can eat these. logan's run 1976
17368.0 They all have names and numbers on them. I wonder what they are? :-()-: "Beloved Husband". "Beloved Wife". What can all that mean? logan's run 1976
17351.0 Exactly four steps now. Let me lead you. Now to the right. It's narrow here, you'll have to get behind me. :-()-: How will they know we're coming? :-()-: They're watching us now. They'll let us in when they're sure. logan's run 1976
17324.0 That was a great shot you made. :-()-: Yes. But you look a little rusty to me -- what were you doing, wondering? logan's run 1976
17376.0 What does that water do? :-()-: It's part of the hydrogalvanic system. The ocean tides are changed into energy somehow. :-()-: Is it inside the city? :-()-: Of course. I don't know where... I just took them for granted. It's our only chance. logan's run 1976
17400.0 That's better than gold when it's cold. :-()-: Thank you. Tell me -- what do those words mean? "Beloved husband"... "Beloved son"... "Beloved wife"... :-()-: My father was the husband and my mother was the wife. "Beloved" is a word they used -- to stay together. :-()-: Stay? They lived together all their years? :-()-: Oh, yes... I think... logan's run 1976
17316.0 But you don't know, you just say what everyone says. "One for one. One for one." :-()-: Well, why not!? That's exactly how everything works. How else could the city stay in balance -- You have a better idea? :-()-: No, but at least I wonder sometimes -- instead of doing that "one for one" song of yours. You sound like a sleepteacher with a stuck tape. :-()-: Well the minute you get a better idea you can stop wondering. You know, Logan -- you wonder a lot. Too much for a Sandman. logan's run 1976
17353.0 How do we know this is the right way? :-()-: It's the only way. logan's run 1976
17384.0 Come with us. :-()-: Where are you going? logan's run 1976
17382.0 I'll make the arrangements. :-()-: At least it's over... logan's run 1976
17342.0 Are you here to help me? :-()-: What do you need? logan's run 1976
17364.0 You're right... it must be near now. We'll find it. :-()-: Thirty thousand years didn't last very long, did they? logan's run 1976
17311.0 You are here. I couldn't believe it when they told me. What are you doing? :-()-: Turn this way. No, no... not you... YOU! logan's run 1976
17372.0 What are we promising him? What can we possibly give him? :-()-: He asked if we would bury him when his time comes. :-()-: We can't. We're going back. :-()-: To what? :-()-: I'm going to try and tell people what we've seen and -- :-()-: You're lying! You'll never have the chance to tell anybody anything! You'll be killed the moment you're seen! :-()-: Do you expect me to let things go on without trying to change them?! :-()-: Things won't change... you know that! We can live here together, Logan... have a life as long as his... together! :-()-: Things change! :-()-: You want to go back to kill, is that it?! Now, you'll want to kill your own!!! Kill Sandmen!!! Killing's all you ever...!!! logan's run 1976
17303.0 Is that the wind? Not yet... You must hear my birds sing. :-()-: You know about Sanctuary! I know you do! You have to help us! You don't have a choice! It isn't your decision!! Tell us. :-()-: Never a pair. I have never had a pair. :-()-: Where do you send them? :-()-: You're a beautiful pair. logan's run 1976
17315.0 Why? :-()-: Just wondered... what happens? :-()-: Dunno... flameout maybe. Whatever happens, you can bet it's final. But who would want to find out? logan's run 1976
17385.0 Goodbye. :-()-: Oh, my... logan's run 1976
17299.0 We're hungry do you have anything to eat? :-()-: Anything to eat? logan's run 1976
17380.0 What belongs to the people? :-()-: All this. All of it. :-()-: What people? :-()-: I don't know... but it does. logan's run 1976
17403.0 I don't know who you are. I'd like to thank someone. :-()-: It doesn't matter who we are. Follow the tunnel to the end. :-()-: Will there be someone to tell us where to go from there...? logan's run 1976
17312.0 You should've seen me take my last Runner... perfect. I backed him up against a residence pool and when he terminated... his hand... So now you've seen him... what's the difference awake or asleep? :-()-: Open your eyes once, idiot. It's not every day that a Sandman son is born. I'm telling you, Francis -- that's him! :-()-: Maybe, maybe not. What's the difference? Come on, Logan, let's get out of here before everybody finds out. logan's run 1976
17355.0 I'm afraid. :-()-: It's brighter there... besides, we can't go back. logan's run 1976
17375.0 Beloved husband... :-()-: Beloved wife... logan's run 1976
17310.0 Do you have anything special in mind? :-()-: I don't care... Just get it over with. :-()-: Hurry... hurry... hurry. logan's run 1976
17297.0 Fourteen? Fifteen? Your days are running out. How long can you last? A year. Six months? What happens when you're sixteen and you go green? :-()-: Nothing will happen! I make the rules as I go!! Cubs do what I say! Always have! Always will! I got Cathedral and I'll never let go! :-()-: No cubs over fifteen, Billy! Ever heard of a cub with a green flower? You'll leave Cathedral then, Billy, when you're on green, because they won't let a green stay here. If you try to stay the young ones will gut-rip you apart! :-()-: Shut up! Shut up your damn mouth! logan's run 1976
17390.0 But there may be a few around somewhere. :-()-: What makes you think so? :-()-: My parents thought so. Mother and Father. You know? :-()-: Mother and -- ? You knew your mother and father? logan's run 1976
17306.0 All right. Now you keep your bargain. :-()-: Wait for the wind! Wait and hear the birds sing over you! :-()-: We're ready. logan's run 1976
17348.0 Just follow -- no matter how it seems... :-()-: But what is this -- why? :-()-: The Cubs. When they're flying on muscle there's no way to catch up. Without the dazzle, they'd just go past us -- -- too fast :-()-: Muscle? I don't know that one. logan's run 1976
17305.0 How do you want us? :-()-: Up there. logan's run 1976
17388.0 How long have you been living here? :-()-: For as long as I can remember. :-()-: What kind of place is this? :-()-: Just a place, I suppose... who knows? logan's run 1976
17332.0 What's wrong, Available? :-()-: Please... No. logan's run 1976
17347.0 They're like beasts. Wild. :-()-: Maybe they're angry because they're grown in meccano-breeders. :-()-: Instead of what? Nine months inside a woman: We're all raised the same but most of us don't become cubs in Cathedral. :-()-: Some people say children need human mothering. :-()-: Insane. Nurseries are better than any mother could be. :-()-: I'm only telling you what I've heard... Haven't you ever wondered what your seed-mother was Like...? :-()-: Uh-uh. :-()-: I have. :-()-: When did you begin to question Lastday? :-()-: I don't remember exactly... except I was a Green. What would you like to relive, Logan? :-()-: Let's see -- how long has it been? logan's run 1976
17378.0 And... and how were you grown? Inside your mother? :-()-: Yes... :-()-: Are you sure? :-()-: Mother and Father said so... you know? logan's run 1976
17319.0 You missed something special. :-()-: Well... you can't have them all. logan's run 1976
17331.0 What's your name? :-()-: I'm Mary 2. :-()-: Where do you live, Mary? :-()-: Here. :-()-: Why aren't you in Nursery? :-()-: I'm very smart. :-()-: When do you go up? :-()-: I never go upstairs. You're a nice old lady. logan's run 1976
17362.0 It all seemed to make sense until Box. :-()-: Do you think he was telling the Truth? logan's run 1976
17389.0 How did you get here? :-()-: I have always been here... :-()-: Are there any other humans? :-()-: Gracious... no. :-()-: Have any other people ever passed through? logan's run 1976
17346.0 I'd rather be with you. :-()-: That's nice. logan's run 1976
17309.0 I designed it myself. What'll it be... a face job or a full-body job? :-()-: Just the face. :-()-: Fine... Holly will get you ready. You're in good hands, believe me. logan's run 1976
17383.0 Of course... that's settled then. But just you remember your promise... :-()-: We'll remember. But that's a long time off... logan's run 1976
17300.0 How do you think we got here??!! :-()-: You walked in. I saw you. Don't you remember? logan's run 1976
17314.0 You need a lift. Let's go to Arcade and celebrate... your alert successor... Logan-6. :-()-: Has anyone ever broken in to where the babies are? :-()-: Not in my time... logan's run 1976
17394.0 May we stay here for a while? We'd like to rest. :-()-: Of course you can stay. This belongs to the people. logan's run 1976
17298.0 Are you too startled? Am I too removed from your ken? I'm neither machine nor man... but a perfect fusion of the two... and better than either. No human sculptor could match this greatness... don't you agree? :-()-: All right -- what are you? :-()-: Your turn. logan's run 1976
17317.0 Did you ever see Francis-8? :-()-: I never even visited Nursery before tonight. When you wonder, it slows you up -- you know? logan's run 1976
17336.0 What Quad do you live in? :-()-: K. :-()-: You're sure you don't want to try? logan's run 1976
17339.0 You can have any woman in the city. What do you really want? :-()-: You know. :-()-: I don't believe you. There has to be more. :-()-: All right. logan's run 1976
17327.0 What's going on, Logan?! :-()-: It has nothing to do with you. :-()-: What are you talking about?! I saw you let a Runner go? I saw you, Logan?? Tell me!! logan's run 1976
17371.0 There's a Sanctuary... there is! :-()-: You want there to be one... that doesn't... :-()-: There has to be! I know it exists! It has to!! :-()-: No, there doesn't. Not really -- just so many want it to exist... so many who don't want to die... want it so much that a place called Sanctuary becomes "real". But it doesn't exist. It never existed. Just the hope. :-()-: You're wrong!! It has to be!! It Just has to be!! logan's run 1976
17386.0 Hello, Sandman. :-()-: Hello. :-()-: Do you want to see Doc? logan's run 1976
17391.0 Where are they? :-()-: Dead... they're dead... and buried. logan's run 1976
17356.0 I don't know what's going to happen to us Logan but -- Are you glad you didn't kill him? :-()-: It doesn't make any difference anymore. :-()-: You're really one of us now, aren't you? :-()-: You knew that I wasn't before, didn't you? Why did you stay with me? :-()-: I wanted to... ...And you... what made you kill Sandmen? :-()-: I had to. I did kill... for the first time in my life I killed. :-()-: Because you felt like a Runner, didn't you. :-()-: I guess so... I know I felt something I never felt before... and I didn't like it... not a bit. I'll tell you one thing... Sanctuary better be worth it. That's the last place for me to live now. :-()-: For us. logan's run 1976
17395.0 Are you ready to put him in? :-()-: Not yet. :-()-: All right. logan's run 1976
17392.0 They're beautiful. May I have one too please? :-()-: No -- I'm sorry. It's not possible. :-()-: It isn't fair. I'll give you one of my favorite cats... a Jellicle cat. "Jellicle cats have cheerful faces, Jellicle cats have bright black eyes; They like to practice their airs and graces And wait for the Jellicle Moon to rise." :-()-: I'm sorry but I don't have anything to give you. logan's run 1976
17335.0 But if you're one of the misfits... that's where I come in. :-()-: I didn't say that I would run... I just... :-()-: Are you a 5 or a 6? :-()-: Six. I go red next year. :-()-: You're years away... I don't know why you're thinking of these things, much less talking about them. Want to try? logan's run 1976
17350.0 Are you taking me to them? :-()-: Yes. I don't know what else to do -- with him following us. Why do you keep running from your -- :-()-: Because he's my friend -- and I don't want to be killed by him -- or anyone. :-()-: He's good, isn't he? :-()-: Will he find us and kill us? Yes... or one of the others. You know there's only one place to go now... :-()-: They won't believe us. :-()-: I'd rather take my chances with them... than with Francis. :-()-: They won't listen. :-()-: You think Sandmen will? There's no other way for me, :-()-: We'll convince them. logan's run 1976
17398.0 Thousands and thousands... as many as my cats? :-()-: More... many more. :-()-: And all alive you say? logan's run 1976
17307.0 How did they get in here? :-()-: Regular storage procedure... the same as the other food... The other food stopped coming and they started. :-()-: What other food? :-()-: Fish and plankton, sea greens and protein from the sea. It's all here -- ready -- fresh as harvest day. Fish and plankton, sea greens and protein from the sea... And then it stopped coming and they... ...came instead. So I store them here. I'm ready. And you're ready. It's my Job -- protein, plankton, grass from the sea. logan's run 1976
17326.0 What the hell took you so long? :-()-: Did you ever see anybody renew? :-()-: I think you've been skulling out too much. First Nursery and now stupid questions. :-()-: Did you? :-()-: Of course. :-()-: Anybody we know? :-()-: Look... why don't you get into the water... you need it... more than I do. :-()-: I'm fine... See you... :-()-: At Carousel tonight? logan's run 1976
17323.0 Who invited you? :-()-: I'm in my party mood. logan's run 1976
17343.0 What're you going to do? :-()-: That's tomorrow. :-()-: I wish I could help you. :-()-: Maybe you'll think of something... :-()-: I wish I knew what you think I know. logan's run 1976
17320.0 The damned Yellows are getting out of hand. Those three ought to be in Cathedral. No business scrambling in Arcade... :-()-: What an old, old man you're getting to be, Francis. Weren't you ever a Yellow? I bet you were even wilder than -- come on, Sandman. logan's run 1976
17328.0 Why did you do that??!! :-()-: I didn't do anything, Francis! They've made us believe that... :-()-: Why did you do that???!!! logan's run 1976
17404.0 Someone will follow. When you come to the lock, he will tell you how to go on the other side. Jessica may go with you as far as the lock. :-()-: No. Jessica goes back now. Take her back. Now! Go on back. Back outside, Jessica. logan's run 1976
17318.0 I don't know what makes you so curious. You have any idea who his seed-mother was? :-()-: Of course not! I'm curious, not sick. logan's run 1976
17321.0 You should have been with us in Nursery, Daniel. I'm positive I recognized him -- :-()-: Come on. I don't want to miss the filing-in. There'll be some I know tonight, I think... logan's run 1976
24229.0 Seymour Scagnetti. :-()-: Scagnetti? Oh shit, I hear he's a motherfucker. :-()-: He is a motherfucker. He won't let me leave the halfway house till I get some piece of shit job. :-()-: You're coming back to work for us, right? :-()-: I wanna. But I gotta show this asshole I got an honest-to-goodness job before he'll let me move out on my own. I can't work for you guys and be worried about gettin' back before ten o'clock curfew. reservoir dogs 1992
24241.0 Didn't I tell ya not to worry? Vic was worried. :-()-: Me and you'll drive down to Long Beach tomorrow. I'll introduce you to Matthews, tell him what's going on. reservoir dogs 1992
24291.0 That fucking bastard! That fucking sick fucking bastard! :-()-: Marvin, I need you to hold on. There's officers positioned and waiting to move in a block away. :-()-: What the fuck are they waiting for? That motherfucker cut off my ear! He slashed my face! I'm deformed! :-()-: And I'm dying. They don't know that. All they know is they're not to make a move until Joe Cabot shows up. I was sent undercover to get Cabot. You heard 'em, they said he's on his way. Don't pussy out on me now, Marvin. We're just gonna sit here and bleed until Joe Cabot sticks his fuckin' head through that door. reservoir dogs 1992
24295.0 Follow you where? :-()-: Down to my car. :-()-: Why? :-()-: It's a surprise. reservoir dogs 1992
24249.0 The black Beverly Hills. I knew this lady from Ladora Heights once. "Hi, I'm from Ladora Heights, it's the black Beverly Hills." :-()-: It's not the black Beverly Hills, it's the black Palos Verdes. Anyway, this chick, Elois, was a man-eater- upper. I bet every guy who's ever met her has jacked off to her at least once. You know who she looked like? Christie Love. 'Member that TV show "Get Christie Love"? She was a black female cop. She always used to say "You're under arrest, sugar." :-()-: I was in the sixth grade when that show was on. I totally dug it. What the fuck was the name of the chick who played Christie Love? :-()-: Pam Grier. :-()-: No, it wasn't Pam Grier, Pam Grier was the other one. Pam Grier made the movies. Christie Love was like a Pam Grier TV show, without Pam Grier. :-()-: What the fuck was that chick's name? Oh this is just great, I'm totally fuckin' tortured now. :-()-: Well, whoever she was, Elois looked like her. So one night I walk into the club, and no Elois. Now the bartender was a wetback, he was a friend of mine, his name was Carlos. So I asked him "Hey, Carlos, where's Lady E tonight?" Well apparently Lady E was married to this real piece of dog shit. I mean a real animal. And apparently he would so things to her. reservoir dogs 1992
24263.0 How's freedom kid, pretty fuckin' good, ain't it? :-()-: It's a change. :-()-: Ain't that a sad truth. Remy Martin? :-()-: Sure. :-()-: Take a seat. reservoir dogs 1992
24303.0 I ain't going anywhere. I'm right here. I'm not gonna leave ya. :-()-: Larry, I'm so scared, would you please hold me. reservoir dogs 1992
24245.0 Brown's dead, we don't know about Blue. :-()-: Nobody saw what happened to Mr. Blue? reservoir dogs 1992
24297.0 You talked to Nice Guy Eddie? Why the fuck didn't you say that in the first place? :-()-: You didn't ask. :-()-: Hardy-fuckin-har. What did he say? :-()-: Stay put. Okay, fellas, take a look at the little surprise I brought you. reservoir dogs 1992
24285.0 He was the only one I wasn't a hundred percent on. I should have my fucking head examined for goin' forward when I wasn't a hundred percent. But he seemed like a good kid, and I was impatient and greedy and all the things that fuck you up. :-()-: That's your proof? :-()-: You don't need proof when you got instinct. I ignored it before, but not no more. reservoir dogs 1992
24282.0 What's the cut, poppa? :-()-: Juicy, junior, real juicy. reservoir dogs 1992
24259.0 Let's go over it. Where are you? :-()-: I stand outside and guard the door. I don't let anybody come in or go out. :-()-: Mr. Brown? :-()-: Mr. Brown stays in the car. He's parked across the street till I give him the signal, then he pulls up in front of the store. :-()-: Mr. Blonde and Mr. Blue? :-()-: Crowd control. They handle customers and employees in the display area. reservoir dogs 1992
24264.0 Who's your parole officer? :-()-: A guy named Scagnetti. Seymour Scagnetti. :-()-: How is he? :-()-: Fuckin' asshole, won't let me leave the halfway house. :-()-: Never ceases to amaze me. Fuckin' jungle bunny goes out there, slits some old woman's throat for twenty- five cents. Fuckin' nigger gets Doris Day as a parole officer. But a good fella like you gets stuck with a ball-bustin' prick. reservoir dogs 1992
24237.0 Joe, you're making a terrible mistake I can't let you make. :-()-: Stop pointing your fuckin' gun at daddy! reservoir dogs 1992
24247.0 What does it matter who stays with the cop? We ain't lettin' him go. Not after he's seen everybody. You should've never took him outta your trunk in the first place. :-()-: We were trying to find out what he knew about the set up. :-()-: There is no fuckin' set up! Look, this is the news. Blondie, you stay here and take care of them two. White and Pink come with me, 'cuz if Joe gets here and sees all those fucking cars parked out front, he's going to be as mad at me as he is at you. reservoir dogs 1992
24314.0 Gun shot. :-()-: Oh that's just fucking great! Where's Brown? :-()-: Dead. :-()-: Goddamn, goddamn! How did he die? :-()-: How the fuck do you think? The cops shot him. :-()-: Oh this is bad, this is so bad. Is it bad? :-()-: As opposed to good? :-()-: This is so fucked up. Somebody fucked us big time. :-()-: You really think we were set up? :-()-: You even doubt it? I don't think we got set up, I know we got set up! I mean really, seriously, where did all those cops come from, huh? One minute they're not there, the next minute they're there. I didn't hear any sirens. The alarm went off, okay. Okay, when an alarm goes off, you got an average of four minutes response time. Unless a patrol car is cruising that street, at that particular moment, you got four minutes before they can realistically respond. In one minute there were seventeen blue boys out there. All loaded for bear, all knowing exactly what the fuck they were doing, and they were all just there! Remember that second wave that showed up in the cars? Those were the ones responding to the alarm. But those other motherfuckers were already there, they were waiting for us. You haven't thought about this? :-()-: I haven't had a chance to think. First I was just trying to get the fuck outta there. And after we got away, I've just been dealin' with him. :-()-: Well, you better start thinking about it. Cause I, sure as fuck, am thinking about it. In fact, that's all I'm thinking about. I came this close to just driving off. Whoever set us up, knows about this place. There could've been cops sitting here waiting for me. For all we know, there's cops, driving fast, on their way here now. :-()-: Let's go in the other room... reservoir dogs 1992
24319.0 I gotta take a squirt, where's the commode in this dungeon? :-()-: Go down the hall, turn left, up those stairs, then turn right. reservoir dogs 1992
24286.0 Don't worry, Eddie. Me and Larry have been friends a long time, he ain't gonna shoot. We like each other too much. :-()-: Joe, if you kill that man, you die next. Repeat, if you kill that man, you die next! reservoir dogs 1992
24254.0 Didja use the commode story? :-()-: Fuckin' A. I tell it real good, too. reservoir dogs 1992
24274.0 Yeah, Mr. Pink sounds like Mr. Pussy. Tell you what, let me be Mr. Purple. That sounds good to me, I'm Mr. Purple. :-()-: You're not Mr. Purple, somebody from another job's Mr. Purple. You're Mr. Pink. reservoir dogs 1992
24334.0 Who cares what your name is? Who cares if you're Mr. Pink, Mr. Purple, Mr. Pussy, Mr. Piss... :-()-: Oh that's really easy for you to say, you're Mr. White. You gotta cool-sounding name. So tell me, Mr. White, if you think "Mr. Pink" is no big deal, you wanna trade? reservoir dogs 1992
24308.0 C'mon, throw in a buck. :-()-: Uh-uh. I don't tip. :-()-: Whaddaya mean you don't tip? :-()-: I don't believe in it. :-()-: You don't believe in tipping? reservoir dogs 1992
24246.0 I take it this is the bastard you told me about. Why the hell are you beating on him? :-()-: So he'll tell us who the fuck set us up. :-()-: Would you stop it with that shit! You beat on this prick enough, he'll tell ya he started the Chicago fire. That don't necessarily make it so. Okay, first things fucking last, where's the shit? Please tell me somebody brought something with them. :-()-: I got a bag. I stashed it till I could be sure this place wasn't a police station. :-()-: Well, let's go get it. We also gotta get rid of all those cars. It looks like Sam's hot car lot outside. You stay here and babysit Orange and the cop. You two take a car each, I'll follow ya. You ditch it, I'll pick you up, then we'll pick up the stones. And while I'm following you, I'll arrange for some sort of a doctor for our friend. reservoir dogs 1992
24265.0 I just want you to know, Joe, how much I appreciate your care packages on the inside. :-()-: What the hell did you expect me to do? Just forget about you? :-()-: I just wanted you to know, they meant a lot. :-()-: It's the least I could do Vic. I wish I coulda done more. Vic. Toothpick Vic. Tell me a story? What're your plans? :-()-: Well, what I wanna do is go back to work. But I got this Scagnetti prick deep up my ass. He won't let me leave the halfway house till I get some piece of shit job. My plans have always been to be part of the team again. reservoir dogs 1992
24320.0 So, is he dead or what? :-()-: He ain't dead. :-()-: So what is it? :-()-: I think he's just passed out. :-()-: He scared the fuckin' shit outta me. I thought he was dead fer sure. reservoir dogs 1992
24283.0 What the fuck are you talking about? :-()-: That piece of shit. Workin' with the cops. reservoir dogs 1992
24242.0 Nuts. We got a big meeting in Vegas coming up. And we're kinda just gettin ready for that right now. :-()-: Let Nice Guy set you up at Long Beach. Give ya some cash, get that Scagnetti fuck off your back, and we'll be talking to ya. :-()-: Daddy, I got an idea. Now just hear it out. I know you don't like to use any of the boys on these jobs, but technically, Vic ain't one of the boys. He's been gone for four years. He ain't on no one's list. Ya know he can handle himself, ya know you can trust him. reservoir dogs 1992
24315.0 What the fuck am I doing here? I felt funny about this job right off. As soon as I felt it I should said "No thank you", and walked. But I never fucking listen. Every time I ever got burned buying weed, I always knew the guy wasn't right. I just felt it. But I wanted to believe him. If he's not lyin' to me, and it really is Thai stick, then whoa baby. But it's never Thai stick. And I always said if I felt that way about a job, I'd walk. And I did, and I didn't, because of fuckin' money! :-()-: What's done is done, I need you cool. Are you cool? :-()-: I'm cool. :-()-: Splash some water on your face. Take a breather. reservoir dogs 1992
24287.0 Larry, I'm gonna kill him. :-()-: Goddamn you, Joe, don't make me do this! :-()-: Larry, I'm askin' you to trust me on this. :-()-: Don't ask me that. :-()-: I'm not askin', I'm betting. reservoir dogs 1992
24278.0 Give me this fucking thing. :-()-: What the fuck do you think you're doin'? Give me my book back! :-()-: I'm sick of fuckin' hearin' it Joe; I'll give it back when we leave. :-()-: Whaddaya mean, give it to me when we leave, give it back now. :-()-: For the past fifteen minutes now, you've just been droning on with names. "Toby... Toby... Toby... Toby Wong... Toby Wong... Toby Chung... fuckin' Charlie Chan." I got Madonna's big dick outta my right ear, and Toby Jap I-don't-know-what, outta my left. :-()-: What do you care? :-()-: When you're annoying as hell, I care a lot. :-()-: Give me my book. :-()-: You gonna put it away? :-()-: I'm gonna do whatever I wanna do with it. :-()-: Well, then, I'm afraid I'm gonna have to keep it. reservoir dogs 1992
24284.0 Joe, I don't know what you think you know, but you're wrong. :-()-: Like hell I am. :-()-: Joe, trust me on this, you've made a mistake. He's a good kid. I understand you're hot, you're super-fuckin' pissed. We're all real emotional. But you're barking up the wrong tree. I know this man, and he wouldn't do that. :-()-: You don't know jack shit. I do. This rotten bastard tipped off the cops and got Mr. Brown and Mr. Blue killed. reservoir dogs 1992
24300.0 Hey, just cancel that shit right now! You're hurt. You're hurt really fucking bad, but you ain't dying. :-()-: All this blood is scaring the shit outta me. I'm gonna die, I know it. :-()-: Oh excuse me, I didn't realize you had a degree in medicine. Are you a doctor? Are you a doctor? Answer me please, are you a doctor? :-()-: No, I'm not! reservoir dogs 1992
24307.0 Have you guys been listening to K- BILLY's super sounds of the seventies weekend? :-()-: Yeah, it's fuckin' great isn't it? :-()-: Can you believe the songs they been playin'? :-()-: No, I can't. You know what I heard the other day? "Heartbeat-It's Lovebeat," by little Tony DeFranco and the DeFranco Family. I haven't heard that since I was in fifth fuckin' grade. :-()-: When I was coming down here, I was playin' it. And "The Night the Lights Went Out in Georgia" came on. Now I ain't heard that song since it was big, but when it was big, I heard it a million-trillion times. I'm listening to it this morning, and this was the first time I ever realized that the lady singing the song, was the one who killed Andy. reservoir dogs 1992
24228.0 You fuckin' wish. :-()-: You tried to fuck me in my father's office, you sick bastard. Look, Vic, whatever you wanna do in the privacy of your own home, go do it. But don't try to fuck me. I don't think of you that way. I mean, I like you a lot - :-()-: Eddie, if I was a pirate, I wouldn't throw you to the crew. :-()-: No, you'd keep me for yourself. Four years fuckin' punks in the ass made you appreciate prime rib when you get it. :-()-: I might break you, Nice Guy, but I'd make you my dog's bitch. You'd be suckin' the dick and going down on a mangy T-bone hound. :-()-: Now ain't that a sad sight, daddy, walks into jail a white man, walks out talkin' like a nigger. It's all that black semen been shootin' up his butt. It's backed up into his brain and comes out of his mouth. reservoir dogs 1992
24293.0 So, talk. :-()-: We think we got a rat in the house. reservoir dogs 1992
24322.0 Assuming we can trust Joe, how we gonna get in touch with him? He's supposed to be here, but he ain't, which is making me nervous about being here. Even if Joe is on the up and up, he's probably not gonna be that happy with us. Joe planned a robbery, but he's got a blood bath on his hands now. Dead cops, dead robbers, dead civilians... Jesus Christ! I tend to doubt he's gonna have a lot of sympathy for our plight. If I was him, I'd try and put as much distance between me and this mess an humanly possible. :-()-: Before you got here, Mr. Orange was askin' me to take him to a hospital. Now I don't like turning him over to the cops, but if we don't, he's dead. He begged me to do it. I told him to hold off till Joe got here. :-()-: Well Joe ain't gettin' here. We're on our own. Now, I don't know a goddamn body who can help him, so if you know somebody, call 'em. :-()-: I don't know anybody. :-()-: Well, I guess we drop him off at the hospital. Since he don't know nothin' about us, I say it's his decision. :-()-: Well, he knows a little about me. :-()-: You didn't tell him your name, did ya? :-()-: I told him my first name, and where I'm from. reservoir dogs 1992
24267.0 Toby... who the fuck is Toby? Toby... Toby... think... think... think... :-()-: It's not about a nice girl who meets a sensitive boy. Now granted that's what "True Blue" is about, no argument about that. reservoir dogs 1992
24304.0 Look, I don't wanna be a fly in the ointment, but if help doesn't come soon, I gotta see a doctor. I don't give a fuck about jail, I just don't wanna die. :-()-: You're not gonna fucking die, all right? :-()-: I wasn't born yesterday. I'm hurt, and I'm hurt bad. :-()-: It's not good... :-()-: Hey, bless your heart for what you're trying to do. I was panicking for a moment, but I've got my senses back now. The situation is, I'm shot in the belly. And without medical attention, I'm gonna die. :-()-: I can' take you to a hospital. :-()-: Fuck jail! I don't give a shit about jail. But I can't die. You don't have to take me in. Just drive me up to the front, drop me on the sidewalk. I'll take care of myself. I won't tell them anything. I swear to fucking god, I won't tell 'em anything. Look in my eyes, look right in my eyes. I-won't-tell-them-anything. You'll be safe. :-()-: Lie back down, and try to - :-()-: I'm going to die! I need a doctor! I'm begging you, take me to a doctor. reservoir dogs 1992
24276.0 Mr. Blue's dead? :-()-: Dead as Dillinger. reservoir dogs 1992
24235.0 I don't buy it. It doesn't make sense. :-()-: It makes perfect fuckin' sense to me. Eddie, you didn't see how he acted during the job, we did. reservoir dogs 1992
24321.0 He will be dead fer sure, if we don't get him to a hospital. :-()-: We can't take him to a hospital. :-()-: Without medical attention, this man won't live through the night. That bullet in his belly is my fault. Now while that might not mean jack shit to you, it means a helluva lot to me. And I'm not gonna just sit around and watch him die. :-()-: Well, first things first, staying here's goofy. We gotta book up. :-()-: So what do you suggest, we go to a hotel? We got a guy who's shot in the belly, he can't walk, he bleeds like a stuck pig, and when he's awake, he screams in pain. :-()-: You gotta idea, spit it out. :-()-: Joe could help him. If we can get in touch with Joe, Joe could get him to a doctor, Joe could get a doctor to come and see him. reservoir dogs 1992
24226.0 I asked you a question. Are you clear about that? :-()-: Yes. :-()-: Now I'm not gonna bullshit you. I don't really care about what you know or don't know. I'm gonna torture you for awhile regardless. Not to get information, but because torturing a cop amuses me. There's nothing you can say, I've heard it all before. There's nothing you can do. Except pray for a quick death, which you ain't gonna get. reservoir dogs 1992
24234.0 I don't know what he did to her, but she got even. :-()-: Was he all pissed off? reservoir dogs 1992
24251.0 Say "hello" to a motherfucker who's inside. Cabot's doing a job and take a big fat guess who he wants on the team? :-()-: This better not be some Freddy joke. reservoir dogs 1992
24328.0 Is that supposed to be funny? :-()-: We don't think this place is safe. :-()-: This place just ain't secure anymore. We're leaving, and you should go with us. reservoir dogs 1992
24273.0 Why can't we pick out our own colors? :-()-: I tried that once, it don't work. You get four guys fighting over who's gonna be Mr. Black. Since nobody knows anybody else, nobody wants to back down. So forget it, I pick. Be thankful you're not Mr. Yellow. reservoir dogs 1992
24301.0 Say-the-goddamn-words: You're gonna be okay! :-()-: I'm okay. :-()-: Correct. reservoir dogs 1992
24333.0 Okay, Mr. Expert. If this is such a truism, how come every nigger I know treats his woman like a piece of shit? :-()-: I'll make you a bet that those same damn niggers who were showin' their ass in public, when their bitches get 'em home, they chill the fuck out. :-()-: Not these guys. :-()-: Yeah, those guys too. reservoir dogs 1992
24311.0 These ladies aren't starvin' to death. They make minimum wage. When I worked for minimum wage, I wasn't lucky enough to have a job that society deemed tipworthy. :-()-: Ahh, now we're getting down to it. It's not just that he's a cheap bastard - reservoir dogs 1992
24289.0 Aren't you? :-()-: I don't have the slightest fuckin' idea what you're talkin' about. reservoir dogs 1992
24253.0 What kinds questions did Cabot ask? :-()-: Where I was from, who I knew, how I knew Nice Guy, had I done time, shit like that. reservoir dogs 1992
24330.0 I told ya he'd be pissed. :-()-: What are you gonna do about him? reservoir dogs 1992
24227.0 How ya doin', Toothpick? :-()-: Fine, now. :-()-: I'm sorry man, I shoulda picked you up personally at the pen. This whole week's just been crazy. I've had my head up my ass the entire time. :-()-: Funny you should mention it. That's what your father and I been talkin' about. :-()-: That I should've picked you up? :-()-: No. That your head's been up your ass. I walk through the door and Joe says "Vic, you're back, thank god. Finally somebody who knows what the fuck he's doing. Vic, Vic, Vic, Eddie, my son, is a fuck up." And I say "Well, Joe, I coulda told you that." "I'm ruined! He's ruining me! My son, I love him, but he's taking my business and flushing it down the fuckin' toilet! " I'm not tellin' tales out of school. You tell 'im Joe. Tell 'im yourself. reservoir dogs 1992
24294.0 What's this guy's problem? :-()-: What's my problem? Yeah, I gotta problem. I gotta big problem with any trigger-happy madman who almost gets me shot! :-()-: What're you talkin' about? :-()-: That fuckin' shooting spree in the store. :-()-: Fuck 'em, they set off the alarm, they deserve what they got. :-()-: You almost killed me, asshole! If I had any idea what type of guy you were, I never would've agreed to work with you. :-()-: You gonna bark all day, little doggie, or are you gonna bite? :-()-: What was that? I'm sorry, I didn't catch it. Would you repeat it? :-()-: I said: "Are you gonna bark all day, dog, or are you gonna bite." reservoir dogs 1992
24244.0 We were set up, the cops were waiting for us. :-()-: What? Nobody set anybody up. :-()-: The cops were there waitin' for us! :-()-: Bullshit. :-()-: Hey, fuck you man, you weren't there, we were. And I'm tellin' ya, the cops had that store staked out. :-()-: Okay, Mr. Detective, who did it? :-()-: What the fuck d'you think we've been askin' each other? :-()-: And what are your answers? Was it me? You think I set you up? :-()-: I don't know, but somebody did. :-()-: Nobody did. You assholes turn the jewelry store into a wild west show, and you wonder why cops show up. reservoir dogs 1992
24268.0 Hey, fuck all that, I'm making a point here. You're gonna make me lose my train of thought. :-()-: Oh fuck, Toby's that little china girl. reservoir dogs 1992
24323.0 Why! :-()-: I told him where I was from a few days ago. It was just a casual conversation. :-()-: And what was tellin him your name when you weren't supposed to? :-()-: He asked. reservoir dogs 1992
24280.0 I'll take care of this, you guys leave the tip. And when I come back, I want my book back. :-()-: Sorry, it's my book now. :-()-: Blonde, shoot this piece of shit, will ya? reservoir dogs 1992
24230.0 I don't wanna lift crates. :-()-: You don't hafta lift shit. You don't really work there. But as far as the records are concerned, you do. I call up Matthews, the foreman, tell him he's got a new guy. You're on the schedule. You got a timecard, it's clocked in and out for you everyday, and you get a pay check at the end of the week. And ya know dock workers don't do too bad. So you can move into a halfway decent place without Scagnetti thinkin "what the fuck." And if Scagnetti ever wants to make a surprise visit, you're gone that day. That day we sent you to Tustin. We gotta bunch of shit you needed to unload there. You're at the Taft airstrip pickin' up a bunch of shit and bringing it back. Part of your job is goin' different places - and we got places all over the place. reservoir dogs 1992
24279.0 No, she did it. She killed the cheatin' wife, too. :-()-: Who gives a damn? reservoir dogs 1992
24231.0 Holy shit, this guy's all fucked up! :-()-: No shit, he's gonna fuckin' die on us if we don't get him taken care of. reservoir dogs 1992
24329.0 Both of you two assholes knock it the fuck off and calm down! :-()-: So you wanna git bit, huh? :-()-: Cut the bullshit, we ain't on a fuckin' playground! I don't believe this shit, both of you got ten years on me, and I'm the only one actin like a professional. You guys act like a bunch of fuckin' niggers. You ever work a job with a bunch of niggers? They're just like you two, always fightin', always sayin' they're gonna kill one another. :-()-: You said yourself, you thought about takin' him out. :-()-: Then. That time has passed. Right now, Mr. Blonde is the only one I completely trust. He's too fuckin' homicidal to be workin' with the cops. :-()-: You takin' his side? :-()-: Fuck sides! What we need is a little solidarity here. Somebody's stickin' a red hot poker up our asses and we gotta find out whose hand's on the handle. Now I know I'm no piece of shit... And I'm pretty sure you're a good boy... And I'm fuckin positive you're on the level. So let's figure out who's the bad guy. reservoir dogs 1992
24248.0 Go ahead and laugh, you know what I mean. What a while bitch will put up with, a black bitch won't put up with for a minute. They got a line, and if you cross it, they fuck you up. :-()-: I gotta go along with Mr. Pink on this. I've seen it happen. reservoir dogs 1992
24239.0 Now Vic was tellin' me, he's got a parole problem. :-()-: Really? Who's your P.O.? reservoir dogs 1992
24225.0 Alone at last. :-()-: Now where were we? :-()-: I told you I don't know anything about any fucking set up. I've only been on the force eight months, nobody tells me anything! I don't know anything! You can torture me if you want - :-()-: Thanks, don't mind if I do. :-()-: Your boss even said there wasn't a set up. :-()-: First off, I don't have a boss. Are you clear about that? reservoir dogs 1992
24327.0 He got it in the belly. He's still alive, but won't be for long. :-()-: Enough! You better start talkin' to us, asshole, cause we got shit we need to talk about. We're already freaked out, we need you actin freaky like we need a fuckin' bag on our hip. reservoir dogs 1992
24271.0 Because you paid for the breakfast, I'm gonna tip. Normally I wouldn't. :-()-: Whatever. Just throw in your dollar, and let's move. See what I'm dealing with here. Infants. I'm fuckin' dealin' with infants. reservoir dogs 1992
24331.0 He seems all right now, but he went crazy in the store. :-()-: This is what he was doin'. reservoir dogs 1992
24261.0 I'm sorry. :-()-: What? Snap out of it! reservoir dogs 1992
24296.0 For what, the cops? :-()-: Nice Guy Eddie. reservoir dogs 1992
24258.0 Jesus Christ! :-()-: You can do some crazy things with it. reservoir dogs 1992
24277.0 What's that? :-()-: I found this old address book in a jacket I ain't worn in a coon's age. Toby what? What the fuck was her last name? reservoir dogs 1992
24272.0 But he's OK. If he wasn't OK, he wouldn't be here. Okay, let me introduce everybody to everybody. But once again, at the risk of being redundant, if I even think I hear somebody telling or referring to somebody by their Christian name... ...you won't want to be you. Okay, quickly. Mr. Brown, Mr. White, Mr. Blonde, Mr. Blue, Mr. Orange, and Mr. Pink. :-()-: Why am I Mr. Pink? :-()-: Cause you're a faggot, alright? reservoir dogs 1992
24281.0 What's she doin' now? :-()-: She hooked up with Fed McGar, they've done a couple a jobs together. Good little thief. So, explain the telegram. :-()-: Five-man job. Bustin' in and bustin' out of a diamond wholesaler's. :-()-: Can you move the ice afterwards? I don't know nobody who can move ice. :-()-: Not a problem, got guys waitin' for it. But what happened to Marsellus Spivey? Didn't he always move your ice? :-()-: He's doin' twenty years in Susanville. :-()-: What for? :-()-: Bad luck. What's the exposure like? :-()-: Two minutes, tops. It's a tough two minutes. It's daylight, during business hours, dealing with a crowd. But you'll have the guys to deal with the crowd. :-()-: How many employees? :-()-: Around twenty. Security pretty lax. They almost always just deal in boxes. Rough uncut stones they get from the syndicate. On a certain day this wholesaler's gettin' a big shipment of polished stones from Israel. They're like a way station. They are gonna get picked up the next day and sent to Vermont. :-()-: No, they're not. reservoir dogs 1992
24325.0 We ain't taking him to a hospital. :-()-: If we don't, he'll die. :-()-: And I'm very sad about that. But some fellas are lucky, and some ain't. :-()-: That fuckin did it! reservoir dogs 1992
24275.0 Nobody's trading with anybody! Look, this ain't a goddamn fuckin' city counsel meeting! Listen up Mr. Pink. We got two ways here, my way or the highway. And you can go down either of 'em. So what's it gonna be, Mr. Pink? :-()-: Jesus Christ, Joe. Fuckin' forget it. This is beneath me. I'm Mr. Pink, let's move on. reservoir dogs 1992
24318.0 Tagged a couple of cops. Did you kill anybody? :-()-: A few cops. :-()-: No real people? :-()-: Uh-uh, just cops. :-()-: Could you believe Mr. Blonde? :-()-: That was one of the most insane fucking things I've ever seen. Why the fuck would Joe hire somebody like that? :-()-: I don't wanna kill anybody. But if I gotta get out that door, and you're standing in my way, one way of the other, you're gettin' outta my way. :-()-: That's the way I look at it. A choice between doin' ten years, and takin' out some stupid motherfucker, ain't no choice at all. But I ain't no madman either. What the fuck was Joe thinkin'? You can't work with a guy like that. That motherfucker's unstable. What do you think? Do you think he panicked, or ya think he's just trigger-happy? :-()-: I think he's a sick fuckin' maniac! We're awful goddamn lucky he didn't tag us, when he shot up the place. I came this fucking close - to taking his ass out myself. Everybody panics. When things get tense, everybody panics. Everybody. I don't care what your name is, you can't help it. It's human nature. But ya panic on the inside. Ya panic in your head. Ya give yourself a couple a seconds of panic, then you get a grip and deal with the situation. What you don't do, is shoot up the place and kill everybody. :-()-: What you're supposed to do is act like a fuckin' professional. A psychopath is not a professional. You can't work with a psychopath, 'cause ya don't know what those sick assholes are gonna do next. I mean, Jesus Christ, how old do you think that black girl was? Twenty, maybe twenty-one? :-()-: Did ya see what happened to anybody else? :-()-: Me and Mr. Orange jumped in the car and Mr. Brown floored it. After that, I don't know what went down. :-()-: At that point it became every man for himself. As far as Mr. Blonde or Mr. Blue are concerned, I ain't got the foggiest. Once I got out, I never looked back. :-()-: What do you think? :-()-: What do I think? I think the cops caught them, or killed 'em. :-()-: Not even a chance they punched through? You found a hole. :-()-: Yeah, and that was a fucking miracle. But if they did get away, where the fuck are they? :-()-: You don't think it's possible, one of them got a hold of the diamonds and pulled a - :-()-: Nope. :-()-: How can you be so sure? :-()-: I got the diamonds. :-()-: Where? :-()-: I got 'em, all right? :-()-: Where? Are they out in the car? :-()-: No, they're not in the car. No, I don't have them on me. Ya wanna go with me and get 'em? Yes, we can go right now. But first listen to what I'm telling you. We were fuckin' set up! Somebody is in league with the cops. We got a Judas in our midst. And I'm thinkin' we should have our fuckin' heads examined for waiting around here. :-()-: That was the plan, we meet here. :-()-: Then where is everybody? I say the plan became null and void once we found out we got a rat in the house. We ain't got the slightest fuckin' idea what happened to Mr. Blonde or Mr. Blue. They could both be dead or arrested. They could be sweatin' 'em, down at the station house right now. Yeah they don't know the names, but they can sing about this place. I mean, that could be happening right now. As we speak, the cops could be in their cars, drivin' here this minute. :-()-: I swear to god I'm fuckin' jinxed. :-()-: What? :-()-: Two jobs back, it was a four man job, we discovered one of the team was an undercover cop. :-()-: No shit? :-()-: Thank god, we discovered in time. We hadda forget the whole fuckin' thing. Just walked away from it. :-()-: So who's the rat this time? Mr. Blue? Mr. Blonde? Joe? It's Joe's show, he set this whole thing up. Maybe he set it up to set it up. :-()-: I don't buy it. Me and Joe go back a long time. I can tell ya straight up, Joe definitely didn't have anything to do with this bullshit. :-()-: Oh, you and Joe go back a long time. I known Joe since I was a kid. But me saying Joe definitely couldn't have done it is ridiculous. I can say I definitely didn't do it, cause I know what I did or didn't do. But I can't definitely say that about anybody else, 'cause I don't definitely know. For all I know, you're the rat. :-()-: For all I know, you're the rat. :-()-: Now you're using your head. For all we know, he's the rat. :-()-: That kid in there is dying from a fuckin' bullet that I saw him take. So don't be calling him a rat. :-()-: Look, asshole, I'm right! Somebody's a fuckin' rat. How many times do I hafta say it before it sinks in your skull? reservoir dogs 1992
24292.0 Joe, you want me to shoot him for you? :-()-: Shit, you shoot me in a dream, you better wake up and apologize. reservoir dogs 1992
24310.0 I'd go over twelve percent for that. :-()-: Look, I ordered coffee. Now we've been here a long fuckin' time, and she's only filled my cup three times. When I order coffee, I want it filled six times. reservoir dogs 1992
24324.0 We had just gotten away from the cops. He just got shot. It was my fuckin' fault he got shot. He's a fuckin' bloody mess - he's screaming. I swear to god, I thought we was gonna die right then and there. I'm tryin' to comfort him, telling him not to worry, he's gonna be okay, I'm gonna take care of him. And he asked me what my name was. I mean, the man was dyin' in my arms. What the fuck was I supposed to tell him, "Sorry, I can't give out that information, it's against the rules. I don't trust you enough."? Maybe I shoulda, but I couldn't. :-()-: Oh, I don't doubt is was quite beautiful - :-()-: Don't fuckin' patronize me. :-()-: One question: Do they have a sheet on you, where you told him you're from? :-()-: Of course. :-()-: Well that's that, then. I mean, I was worried about mug shot possibilities already. But now he knows: what you look like, what your first name is, where you're from and what your specialty is. They ain't gonna hafta show him a helluva lot of pictures for him to pick you out. That's it right, you didn't tell him anything else that could narrow down the selection? :-()-: If I have to tell you again to back off, me an you are gonna go round and round. reservoir dogs 1992
24290.0 I know. :-()-: You do? :-()-: Your name's Freddy something. :-()-: Freddy Newendyke. :-()-: Frankie Ferchetti introduced us once, about five months ago. :-()-: Shit. I don't remember that at all. :-()-: I do. How do I look? reservoir dogs 1992
24240.0 We can work this out, can't we? :-()-: This isn't all that bad. We can give you a lot of legitimate jobs. Put you on the rotation at Long Beach as a dock worker. reservoir dogs 1992
24269.0 Then one day she meets a John Holmes motherfucker, and it's like, whoa baby. This mother fucker's like Charles Bronson in "The Great Escape." He's diggin' tunnels. Now she's gettin' this serious dick action, she's feelin' something she ain't felt since forever. Pain. :-()-: Chew? Toby Chew? No. :-()-: It hurts. It hurts her. It shouldn't hurt. Her pussy should be Bubble-Yum by now. But when this cat fucks her, it hurts. It hurts like the first time. The pain is reminding a fuck machine what is was like to be a virgin. Hence, "Like a Virgin." reservoir dogs 1992
24312.0 Do you know what this is? It's the world's smallest violin, playing just for the waitresses. :-()-: You don't have any idea what you're talking about. These people bust their ass. This is a hard job. :-()-: So's working at McDonald's, but you don't feel the need to tip them. They're servin' ya food, you should tip em. But no, society says tip these guys over here, but not those guys over there. That's bullshit. reservoir dogs 1992
24260.0 Myself and Mr. Pink? :-()-: You two take the manager in the back and make him give you the diamonds. We're there for those stones, period. Since no display cases are being fucked with, no alarms should go off. We're out of there in two minutes, not one second longer. What if the manager won't give up the diamonds? :-()-: When you're dealing with a store like this, they're insured up the ass. They're not supposed to give you and resistance whatsoever. If you get a customer or an employee who thinks he's Charles Bronson, take the butt of your gun and smash their nose in. Drops 'em right to the floor. Everyone jumps, he falls down, screaming, blood squirts out his nose. Freaks everybody out. Nobody says fuckin' shit after that. You might get some bitch talk shit to ya. But give her a look, like you're gonna smash her in the face next. Watch her shut the fuck up. Now if it's a manager, that's a different story. The managers know better than to fuck around. So if one's givin' you static, he probably thinks he's a real cowboy. So what you gotta do is break that son-of-a-bitch in two. If you wanna know something and he won't tell you, cut off one of his fingers. The little one. Then you tell 'im his thumb's next. After that he'll tell ya if he wears ladies underwear. I'm hungry, let's get a taco. reservoir dogs 1992
24302.0 Just hold on buddy boy. Hold on, and wait for Joe. I can't do anything for you, but when Joe gets here, which should be anytime now, he'll be able to help you. We're just gonna sit here, and wait for Joe. Who are we waiting for? :-()-: Joe. :-()-: Bet your sweet ass we are. reservoir dogs 1992
24313.0 Waitressing is the number one occupation for female non-college graduates in this country. It's the one job basically any woman can get, and make a living on. The reason is because of tips. :-()-: Fuck all that. reservoir dogs 1992
24270.0 Wong? :-()-: Fuck you, wrong. I'm right! What the fuck do you know about it anyway? You're still listening to Jerry- fucking-Vale. :-()-: Not wrong, dumb ass, Wong! You know, like the Chinese name? reservoir dogs 1992
24298.0 Because this guy's a fucking psycho. And if you think Joe's pissed at us, that ain't nothing compared to how pissed off I am at him, for puttin' me in the same room as this bastard. :-()-: You see what I been puttin' up with? As soon as I walk through the door I'm hit with this shit. I tell 'm what you told me about us stayin' put and Mr. White whips out his gun, sticks it in my face, and starts screaming "You motherfucker, I'm gonna blow you away, blah, blah, blah." :-()-: He's the reason the place turned into a shooting gallery. What are you, a silent partner? Fuckin' tell him. reservoir dogs 1992
24252.0 Nice Guy. When we got to the bar... :-()-: ...What bar? :-()-: The Boots and Socks in Gardena. When we got there, I met Joe and a guy named Mr. White. It's a phony name. My name's Mr. Orange. :-()-: You ever seen this motherfucker before? :-()-: Who, Mr. White? :-()-: Yeah. :-()-: No, he ain't familiar. He ain't one of Cabot's soldiers either. He's gotta be from outta town. But Joe knows him real well. :-()-: How can you tell? :-()-: The way they talk to each other. You can tell they're buddies. :-()-: Did the two of you talk? :-()-: Me and Mr. White? :-()-: Yeah. :-()-: A little. :-()-: What about? :-()-: The Brewers. :-()-: The Milwaukee Brewers? :-()-: Yeah. They had just won the night before, and he made a killing off 'em. :-()-: Well, if this crook's a Brewers fan, his ass has gotta be from Wisconsin. And I'll bet you everything from a diddle-eyed Joe to a damned-if-I- know, that in Milwaukee they got a sheet on this Mr. White motherfucker's ass. I want you to go through the mugs of guys from old Milwaukee with a history of armed robbery, and put a name to that face. reservoir dogs 1992
24306.0 Uhuh, uhuh, what's I tell ya? That sick piece of shit was a stone cold psycho. :-()-: You could've asked the cop, if you didn't just kill him. He talked about what he was going to do when he was slicing him up. reservoir dogs 1992
24233.0 Let me tell you guys a story. In one of daddy's clubs there was this black cocktail waitress named Elois. :-()-: Elois? :-()-: Yeah, Elois. E and Lois. We called her Lady E. :-()-: Where was she from, Compton? :-()-: No. She was from Ladora Heights. reservoir dogs 1992
24232.0 Jesus Christ, give me a fuckin' chance to breathe. I got a few questions of my own, ya know. :-()-: You ain't dying, he is. :-()-: I'll call somebody. :-()-: Who? :-()-: A snake charmer, what the fuck d'you think. I'll call a doctor, take care of him, fix 'm right up. No, where's Mr. Brown and Mr. Blue? reservoir dogs 1992
24257.0 ...Her brother usually goes with her, but he's in county unexpectedly. :-()-: What for? :-()-: Traffic tickets gone to warrant. They stopped him for something, found the warrants on 'im, took 'im to jail. She doesn't want to walk around alone with all that weed. Well, I don't wanna do this, I have a bad feeling about it, but she keeps askin' me, keeps askin' me, finally I said okay 'cause I'm sick of listening to it. Well, we're picking this guy up at the train station. reservoir dogs 1992
24326.0 You wanna shoot me, you little piece of shit? Take a shot! :-()-: Fuck you, White! I didn't create this situation, I'm just dealin' with it. You're acting like a first- year fuckin' thief. I'm actin like a professional. They get him, they can get you, they get you, they get closer to me, and that can't happen. And you, you motherfucker, are looking at me like it's my fault. I didn't tell him my name. I didn't tell him where I was from. I didn't tell him what I knew better than to tell him. Fuck, fifteen minutes ago, you almost told me your name. You, buddy, are stuck in a situation you created. So if you wanna throw bad looks somewhere, throw 'em at a mirror. reservoir dogs 1992
24250.0 He's right about the ear, it's hacked off. :-()-: Let me say this out loud, just to get it straight in my mind. According to you, Mr. Blonde was gonna kill you. Then when we came back, kill us, grab the diamonds, and scram. That's your story? I'm correct about that, right? reservoir dogs 1992
24262.0 Just hold on buddy boy. :-()-: I'm sorry. I can't believe she killed me... reservoir dogs 1992
24299.0 I told 'em not to touch the alarm. They touched it. I blew 'em full of holes. If they hadn't done what I told 'em not it, they'd still be alive. :-()-: That's your excuse for going on a kill crazy rampage? :-()-: I don't like alarms. reservoir dogs 1992
24243.0 Daddy, I'm sorry, I don't know what's happening. :-()-: That's okay, Eddie, I do. reservoir dogs 1992
24236.0 The motherfucker killed Vic. :-()-: How do you know all this? reservoir dogs 1992
24288.0 Mr. Pink. :-()-: Mr. Pink? Why? :-()-: He don't tip. :-()-: He don't tip? You don't tip? Why? :-()-: He don't believe in it. :-()-: He don't believe in it? You don't believe in it? :-()-: Nope. :-()-: Shut up! Cough up the buck, ya cheap bastard, I paid for your goddamn breakfast. :-()-: Okay ramblers, let's get to rambling. Wait a minute, who didn't throw in? reservoir dogs 1992
24266.0 That's great, guy, thanks a bunch. When do you think you'll need me for real work? :-()-: Well, it's kinda a strange time right now. Things are kinda - reservoir dogs 1992
24305.0 What happened? :-()-: Blonde went crazy. He slashed the cop's face, cut off his ear and was gonna burn him alive. reservoir dogs 1992
24317.0 Okay, let's go through what happened. We're in the place, everything's going fine. Then the alarm gets tripped. I turn around and all these cops are outside. You're right, it was like, bam! I blink my eyes are they're there. Everybody starts going apeshit. Then Mr. Blonde starts shootin' all the - :-()-: That's not correct. :-()-: What's wrong with it? :-()-: The cops didn't show up after the alarm went off. They didn't show till after Mr. Blonde started shooting everyone. :-()-: As soon as I heard the alarm, I saw the cops. :-()-: I'm telling ya, it wasn't that soon. They didn't let their presence be known until after Mr. Blonde went off. I'm not sayin' they weren't there, I'm sayin' they were there. But they didn't move in till Mr. Blonde became a madman. That's how I know we were set up. You can see that, can't you, Mr. White? :-()-: Look, enough of this "Mr White" shit - :-()-: Don't tell me your name, I don't want to know! I sure as hell ain't gonna tell ya mine. :-()-: You're right, this is bad. How did you get out? :-()-: Shot my way out. Everybody was shooting, so I just blasted my way outta there. reservoir dogs 1992
24255.0 What's this? :-()-: It's a scene. Memorize it. :-()-: What? :-()-: A undercover cop has got to be Marlon Brando. To do this job you got to be a great actor. You got to be naturalistic. You got to be naturalistic as hell. If you ain't a great actor you're a bad actor, and bad acting is bullshit in this job. :-()-: But what is this? :-()-: It's a amusing anecdote about a drug deal. :-()-: What? :-()-: Something funny that happened to you while you were doing a job. :-()-: I gotta memorize all this shit? :-()-: It's like a joke. You remember what's important, and the rest you make your own. The only way to make it your own is to keep sayin' it, and sayin' it, and sayin' it, and sayin' it, and sayin' it. :-()-: I can do that. :-()-: The things you gotta remember are the details. It's the details that sell your story. Now this story takes place in this men's room. So you gotta know the details about this men's room. You gotta know they got a blower instead of a towel to dry your hands. You gotta know the stalls ain't got no doors. You gotta know whether they got liquid or powdered soap, whether they got hot water or not, 'cause if you do your job when you tell your story, everybody should believe it. And if you tell your story to somebody who's actually taken a piss in this men's room, and you get one detail they remember right, they'll swear by you. reservoir dogs 1992
24309.0 I don't even know a Jew who'd have the balls to say that. So let's get this straight. You never ever tip? :-()-: I don't tip because society says I gotta. I tip when somebody deserves a tip. When somebody really puts forth an effort, they deserve a little something extra. But this tipping automatically, that shit's for the birds. As far as I'm concerned, they're just doin' their job. reservoir dogs 1992
24332.0 ...Hey, I know what I'm talkin' about, black women ain't the same as white women. :-()-: There's a slight difference. reservoir dogs 1992
24238.0 Daddy, did ya see that? :-()-: What? :-()-: Guy got me on the ground, tried to fuck me. reservoir dogs 1992
24256.0 Tell me more about Cabot. :-()-: He's a cool guy. A real nice, real funny, real cool guy. reservoir dogs 1992
24316.0 Want a smoke? :-()-: Why not? reservoir dogs 1992
4074.0 A baby? What are we supposed to do with a baby? :-()-: Name it. birthday girl 2001
4094.0 You are not ashamed of it? It's no surprise to want to love. :-()-: No. It's not that. :-()-: Do you believe in love? :-()-: I suppose it's... I mean define your terms. :-()-: It's very strange. How many people are truly themselves with their love? It is the greatest human disaster and it is never in the newspapers. There are no Marches Against Heartache, no Ministries Against Loneliness, no Concerts Against Disappointment. We look away. And still we know in secret that nothing is more important to us. The one thing we all share but don't say. Look John I will show you something. birthday girl 2001
4150.0 My name's Sophia. :-()-: Sophia. Hello Sophia. Mine's still John. birthday girl 2001
4113.0 They go. John. They go. :-()-: What's wrong? :-()-: They go. :-()-: Of course. They go. Yes. Yes. :-()-: They go. birthday girl 2001
4077.0 This is sensitive. Your car. Lovely car. Doesn't necessarily give the right impression. :-()-: Ch... :-()-: To customers approaching the bank from the rear :-()-: Right. :-()-: You can see why it's sensitive? :-()-: Uh... Yes. birthday girl 2001
4149.0 Hurry. I'll wait for you here. :-()-: Right. birthday girl 2001
4091.0 When? :-()-: "I saw you waiting there, by the gate." :-()-: I... :-()-: "I have these uh..." She explains to you... "When I was a little girl my father had these beautiful old glasses." Like... I don't know the word. Like for watching uh... for watching the birds. birthday girl 2001
4129.0 It wasn't what I wanted. :-()-: So what did you want? I think we understand each other, no? :-()-: You don't understand me. :-()-: You don't understand you either. birthday girl 2001
4067.0 Yeah it was okay. :-()-: Yeah. It was quite good actually. Some bits I really liked. :-()-: The sets were good. :-()-: The sets were excellent. Everything was big, you know, all the rubbish, coke cans, sweet wrappers, dustbins, so when you were watching it you felt cat size. It was really clever. birthday girl 2001
4098.0 He says you scare him so much he must go to the toilet in his trousers. John, he is a soldier. A trained killer. We must do what he says. :-()-: What? What does he want? birthday girl 2001
4140.0 You know you can come under the blanket. :-()-: It's alright. birthday girl 2001
4083.0 Hold on. :-()-: Toast first then we talk seriously, I can see you are serious about us. birthday girl 2001
4065.0 Put it in the cases. Split it up. And don't forget you owe me �150. :-()-: What for? :-()-: You know what for. :-()-: No I don't. :-()-: I got you those trousers from Paul Smith. :-()-: I've been buying you stuff all week. I've been buying him stuff all week. :-()-: Such as? birthday girl 2001
4081.0 What did he say? :-()-: He says he feels safe here. birthday girl 2001
4104.0 It's about forty miles from here. I don't know if you've looked at a map, it's close to London but it's a city in itself. A Roman city. It's a nice house. I'm having a problem with ants. I uh... It's the warmer weather. I can't seem to find the nest. Sorry, do you understand "ants"? :-()-: Yes. :-()-: I just can't find a nest. The root of the problem. I've looked everywhere. What's the Russian for ant? Sorry that's a stupid... Sorry. This is strange isn't it. :-()-: Yes. :-()-: I'm pretty nervous. Are you? :-()-: Yes. :-()-: I mean... "Ants." "I've got a problem with ants." birthday girl 2001
4148.0 What does it mean? :-()-: Maybe you will find out. birthday girl 2001
4125.0 I don't want to talk about it. :-()-: Why not? :-()-: Shut up. I'm not listening. :-()-: You don't want to talk about it. :-()-: No. :-()-: Okay we won't talk about it. birthday girl 2001
4152.0 You have excellent English. :-()-: Thanks. :-()-: How do you want to pay? :-()-: Cash. birthday girl 2001
4117.0 You must think... I'm the biggest pillock... In the world. :-()-: No I don't. :-()-: In the world. :-()-: I know you just want to punish me -- :-()-: I do. I want to very badly. :-()-: So you're just going to be vindictive :-()-: In every sense. If at all possible. :-()-: You can't hurt me more than I'm hurt already. :-()-: Well, Nadia, It it's all the same to you, I'd like to give it a bash. birthday girl 2001
4076.0 What. You're what? You're with this creep now. :-()-: Leave him! :-()-: You have. You've actually fallen for this prick. :-()-: No I haven't. birthday girl 2001
4139.0 I don't know. :-()-: What was her name? :-()-: What's your name? birthday girl 2001
4084.0 So hang on. You're both Nadia's cousins? :-()-: Of course not. Alexei, he's is my problem. :-()-: Right. :-()-: We better watch him. He's crazy. :-()-: Right. :-()-: I am actor, he is actor, although he is an actor stroke musician. I just noodle along, I'm not so good. He makes me look like a retard -- He smokes me. I don't mean he smokes me. birthday girl 2001
4099.0 What did he say? Tell me! :-()-: He says you are very sad ridiculous man. I don't agree of course. And that you must pay someone to have sex like a prostitute. Nadia is a prostitute. I'm sorry. :-()-: What does he want. The Russian shithead. What do you want ? :-()-: He wants money. :-()-: Tell him to put the kettle down and I'll give him money. birthday girl 2001
4075.0 What? What are you doing? :-()-: What are you doing here? birthday girl 2001
4085.0 No. :-()-: Right. So I can say he smokes me. So. birthday girl 2001
4134.0 What happened between you and the blonde? :-()-: What? :-()-: The thin... the girl with small eyes. The one in your cupboard. :-()-: It's none of your business. She didn't have small eyes. :-()-: Did she leave you? Come on. It's nothing to be ashamed of. Who did she leave you for? Your best friend? Her boss? A woman? Did she leave you for a woman, John? :-()-: She's dead. birthday girl 2001
4116.0 So what are you going to do? :-()-: I'm going to drink my coffee. Then, we're going to the police station. Where there will be lawyers, loss of job, house, humiliation, gutter press, and probably prison. :-()-: They don't blame you. When a bank employee does this they understand. You get your life back. Anyway I bet you hated that bank. :-()-: Even so I always felt the decision to burst in and rob it very much remained with me. :-()-: Why else would you send off for me? If you just wanted sex just go to a prostitute. :-()-: Well as it turns out I did. birthday girl 2001
4142.0 Yeah. No thanks. :-()-: Please. Why not? :-()-: Because it was a lie. birthday girl 2001
4123.0 So uh... :-()-: Look, if you want to know is he better in bed than you then yes he is. :-()-: Oh Jesus. :-()-: If what you want to know is does he have a bigger cock than you, then yes he does. :-()-: Of course. Of course. Of course he does. Of course. Thank you. Thanks. :-()-: But, you know, so what? birthday girl 2001
4111.0 Syevodnya? :-()-: Syevodnya :-()-: Happy Birthday. Happy Birthday. birthday girl 2001
4096.0 I understand. I'm so sorry :-()-: You can stay tonight. :-()-: I have brought you trouble. Maybe I should have come alone. :-()-: Good night. birthday girl 2001
4070.0 What's this? :-()-: It's nothing. I burnt myself. :-()-: That's not a burn. :-()-: It is. I did it cooking. birthday girl 2001
4122.0 So, uh, Alexei, which I know isn't his name... :-()-: I don't want to talk about him. :-()-: Fine. :-()-: It's none of your business. :-()-: Fine. Absolutely. Must be disappointing though. Must come as a hell of a shock. birthday girl 2001
4108.0 Uh... Are you a giraffe? :-()-: Yes. birthday girl 2001
4106.0 And how was the flight. Sorry, am I speaking too fast for you? :-()-: Yes. birthday girl 2001
4109.0 Today is bath day. :-()-: Sorry? birthday girl 2001
4095.0 How is bank? :-()-: Fine. I thought you were leaving today. :-()-: To be indoors on such a day. It's crime. birthday girl 2001
4105.0 So. Is it different to how you imagined it? :-()-: Yes. :-()-: I bet. What about me? Am I how you imagined? :-()-: Yes. birthday girl 2001
4082.0 I need to know who you are first please. :-()-: Oh. We are Russian. :-()-: Yes. I know. :-()-: Good. :-()-: And... :-()-: And what? You mean from the beginning? Jesus. Can I uh okay, as we say in Russia can I cut a long story short. Okay. Nadia is my little cousin. Except she's not. But we say cousin. This is for you. birthday girl 2001
4097.0 Put the fucking kettle down. :-()-: John. :-()-: Put the fucking kettle down. Tell, Yuri, tell him put it down or I'm going to make him. birthday girl 2001
4146.0 It's not mine. :-()-: It's not mine either. :-()-: It's what you came back for. birthday girl 2001
4124.0 Not the kids type then is he? Not that broody. You must be pretty miffed. :-()-: He will come back. :-()-: Excuse me? :-()-: He left me my passport and ticket. It's pretty clear he wants to see me again. :-()-: Yeah. I tend to tie up and abandon women I really want to see again too. :-()-: No. But you tend to tie them up. birthday girl 2001
4103.0 Is that everything? :-()-: Yes. :-()-: Right. Okay. Good. birthday girl 2001
4093.0 What was that? :-()-: Oh nothing. :-()-: Tell me. :-()-: No. It is too judgmental. :-()-: Tell me what he said. :-()-: He says why did you send to Russia for a wife. birthday girl 2001
4131.0 You know, in Russia, there's no work for women. It's a different world. :-()-: You don't have to say anything :-()-: What? I... I wasn't saying... :-()-: Please, there's no... Oh. :-()-: I wasn't saying anything. :-()-: Then okay. So how old were you when you met him? birthday girl 2001
4126.0 We'll pretend it never happened. :-()-: So. What's it like having to fuck men you hate? :-()-: I don't hate you. :-()-: Okay. Let's... Okay. Okay. You have had sex with people you don't like haven't you? For money. To make money. :-()-: And? What are you saying? :-()-: And. It's wrong. :-()-: And who says what is wrong. :-()-: And that would be Morals. That would be one's own moral sense of decency. :-()-: What's a moral orgasm John? Tell me how it feels exactly. :-()-: So. What then? You just detach sex from everything.. :-()-: Whereas "Wet 'n' Wild" is an emotional journey. "Tied and Tethered". It's pretty moving huh? Like Anna Karenina. :-()-: Listen. I didn't go rooting around in your private stuff. birthday girl 2001
4135.0 I'm sorry. :-()-: I don't know why I said that. She's not dead at all. birthday girl 2001
4128.0 What? :-()-: You... :-()-: I got what I paid for. :-()-: You didn't mind too much. birthday girl 2001
4132.0 Fifteen. You don't know him. He was very kind and strong. :-()-: Yeah. He's a smashing bloke. :-()-: The rest of the world, John, it's not all like St. Albans. :-()-: Thank Christ for that. :-()-: You are pretty naive if you think it is. :-()-: I'm pretty naive? Look at you. You have to do all this, and what have you got to show for it? Nothing. :-()-: I don't have nothing. :-()-: Well what have you got? birthday girl 2001
4080.0 Sorry. You've lost me... :-()-: I'm asking what you're here for. :-()-: What? birthday girl 2001
4069.0 It's enough isn't it? :-()-: What do you mean? :-()-: You know what I mean babe, It's enough. We can stop. :-()-: Do you want to stop? :-()-: Yes. :-()-: We'll stop then. birthday girl 2001
4137.0 She's alive!! She is not dead? :-()-: Laugh it up. birthday girl 2001
4078.0 Hello. :-()-: I thought you could give us the tour this morning. Sort of be our Indian Guide. :-()-: Right. birthday girl 2001
4144.0 Something else. :-()-: Okay. Promise? birthday girl 2001
4118.0 Where's the restroom? :-()-: What? :-()-: I'm going to be sick. Where's the... :-()-: What? No you're not.. :-()-: I'm going... I am... I'm going to be sick. :-()-: No you're not. How... Nice one. How dumb do you think I am ? birthday girl 2001
4086.0 So? :-()-: So I come to England with other actors to make shows, I meet this freak from Novgorod I tell him of you and Chicken and the birthday here we are. birthday girl 2001
4141.0 I've got an hour. Can I buy you a coffee? :-()-: No. I think I better just go. :-()-: Okay. Thank you. :-()-: Whatever. birthday girl 2001
4079.0 How's that? We can't drink our piss can we? :-()-: Hang on hang on, sorry, but like, who are you? :-()-: You must find some glasses, small, for the toast, and some plates. :-()-: What are you doing here? birthday girl 2001
4087.0 What was that? :-()-: I asked her if you were happy to see us. I find it hard to tell with you. :-()-: Yes it's okay. Thank you for the food. birthday girl 2001
4107.0 Do uh... Sorry. Can you follow me? Do you understand what I'm saying? :-()-: Yes. :-()-: Good. Or should I speak slower? :-()-: Yes. :-()-: Do you follow or should I speak slower? :-()-: Yes. birthday girl 2001
4092.0 Binoculars. :-()-: Binoculars. He had these Binoculars he has kept from the war. birthday girl 2001
4138.0 You should stop smoking. You're pregnant. You smoke like a fucking lab dog. :-()-: I'm trying to quit. :-()-: I've got news for you. It's not working. :-()-: I smoke more these days. I smoke more when I'm unhappy. :-()-: Nobody's that unhappy. :-()-: Maybe I want to die. Don't you want me to die? :-()-: I don't want anyone to die. :-()-: Except for Small Eyes. :-()-: Except for Small Eyes. birthday girl 2001
4115.0 Unless. Unless this is part of the routine. You get tied up, stick around, distract me, they both bust in and Steal My Cup Of Coffee. :-()-: It's makes it easier. Okay. :-()-: I don't want to know. :-()-: It makes it faster. If I don't speak to the men, they fall faster. It's pretty obvious why. :-()-: That's a relief. It's nice to know I'm a regular guy. birthday girl 2001
4090.0 She says she has a secret to tell. :-()-: What? birthday girl 2001
4127.0 So what? Do you just switch off in your head or do you imagine you're with him, or what? :-()-: Sometimes. :-()-: Sometimes which? :-()-: Sometimes neither. :-()-: Some... What does that mean? :-()-: There's nothing wrong in liking sex, John. :-()-: I don't like sex. I don't think I'll be having sex ever again. :-()-: Why? :-()-: Well, it's just that the thought trying to charm up an erection in front of a woman, or alone for that matter, makes me want to die. :-()-: So now you hate all women? :-()-: I think it's my safest bet, don't you? :-()-: Oh. I think you will recover okay. I think you got what you paid for. birthday girl 2001
4143.0 Okay. Goodbye. :-()-: Goodbye. birthday girl 2001
4121.0 What? :-()-: I said I don't have any. birthday girl 2001
4130.0 Excuse me? :-()-: Get out :-()-: You are throwing me out. :-()-: Get out. birthday girl 2001
4133.0 Do you know if it's a boy or a girl? :-()-: No. :-()-: Have you had any before? :-()-: No. :-()-: Are you scared? :-()-: Not really. Maybe a little. birthday girl 2001
4102.0 Just leave her alone. :-()-: I'm so sorry. :-()-: Leave her alone. birthday girl 2001
4147.0 Why? :-()-: I'm not asking you to marry me. :-()-: No. What? No. I know. :-()-: It's more like a date. :-()-: It's a long way to go for a date. :-()-: Tell me about it. birthday girl 2001
4072.0 Say thank you. :-()-: Thank you. birthday girl 2001
4120.0 Give me some money. :-()-: I don't have any money. birthday girl 2001
4110.0 I don't understand. :-()-: Happy bath day. birthday girl 2001
4101.0 Oh Jesus. :-()-: He is sure you can do this. Of course you can not. :-()-: Oh Jesus. Of course I can't. birthday girl 2001
4071.0 Do you like it? :-()-: Yeah. birthday girl 2001
4136.0 What? :-()-: I don't know why I said it. I'm sorry. :-()-: She's alive? birthday girl 2001
4112.0 "Frenzy". :-()-: Yes I know. birthday girl 2001
4151.0 Is the flight full? :-()-: I'm sorry Sir. I believe the flight is closed. :-()-: Please check. Is it full? Please could you check. birthday girl 2001
4145.0 You can probably buy them on the flight. :-()-: I'm quitting. This will be my last one. So. Goodbye. :-()-: Goodbye. :-()-: You didn't deserve me John Buckingham. :-()-: Whatever. :-()-: I'm sorry. :-()-: Please. birthday girl 2001
4068.0 Sixty four thousand, eight hundred. :-()-: There's over eighty thousand here. birthday girl 2001
4088.0 So how long will you be in England? :-()-: Plans are for the architects, politicians and so forth. :-()-: You must have a visa or something... :-()-: You're asking for my documents? :-()-: No, no... birthday girl 2001
4119.0 You don't have to do this. I can look after myself. :-()-: Have you got your passport? :-()-: What? :-()-: Shut up. Have you got your passport? :-()-: Yes. birthday girl 2001
4114.0 Oh, I don't know. In my job as Deputy Assistant of New Business at the bank would have to listen to the problems of a great many individuals. This took a lot of understanding and sympathy, to try to work out solutions to their problems. But, you see, I'm not in that line of work anymore. Nowadays I'm a bank robber. :-()-: You don't understand anything. :-()-: I think that about covers it. I think I have grasped the part about you being dumped though. That's got to hurt, I imagine. That's got to smart a bit. I mean strictly in my observer's capacity it seemed you two were getting on Pretty Fucking Famously. birthday girl 2001
4073.0 What? :-()-: You heard what I said. I'm pregnant. I've been throwing up for weeks. birthday girl 2001
4064.0 You first. :-()-: Fifty thousand. Almost exactly. birthday girl 2001
4089.0 She says 'Hello' to you. Go for it John! :-()-: Uh. Do you like England? :-()-: Classic! Thank God. She says 'Yes!' birthday girl 2001
4100.0 He wants a lot of money. :-()-: I'll give him money. Tell him to put the... :-()-: He wants the money from your bank. :-()-: I'll fuckin' give it to him! We'll go down there. :-()-: You don't understand. He wants all the money that is in your bank. :-()-: I've got eight hundred pounds. Oh Jesus. birthday girl 2001
4066.0 When we went to the Hard Rock Cafe. Who paid? When we went to see 'Cats'. Who paid? :-()-: Those aren't presents. That's normal friendship stuff :-()-: I paid for those guitar cases. birthday girl 2001
58970.0 Richard, shut it and keep it down. :-()-: If he hadn't pissed his pants, we woulda won. We fucking had this game. magnolia 1999
59078.0 Linda -- :-()-: What are you doing? :-()-: I've got Frank...Frank Earl's son. He's...he asked me to get him and I did -- :-()-: Hang up the phone. :-()-: No, Linda, you don't understan -- :-()-: PUT THE FUCKIN' PHONE DOWN, HANG IT UP. magnolia 1999
59062.0 ...Jimmy... :-()-: I really don't know. :-()-: But you can't say.... :-()-: <u>I don't know what I've done</u>. :-()-: Yes you do....you do and you won't say. :-()-: ...I don't know... magnolia 1999
58926.0 Why don't they have the same last name? They don't have the same last name. :-()-: I know -- and I can't really explain that, but I have a feeling there's something, some situation between them, like they don't really know each other much or well, something like they don't talk much anymore -- :-()-: Uh-huh. :-()-: Does this sound weird? :-()-: Well I'm not sure why you're calling me. :-()-: There's no number for Frank in any of Earl's stuff and he's pretty out of it -- I mean, like I said, he's dying, y'know. Dying of Cancer. :-()-: What kind of Cancer? :-()-: Brain and Lung. :-()-: My mother had breast cancer. :-()-: It's rough. I'm sorry, did she make it? :-()-: Oh, she's fine. :-()-: Oh that's good. :-()-: It was scary though. :-()-: It's a helluva disease. :-()-: Sure is. So why call me? magnolia 1999
58937.0 ...yeah... :-()-: I'm sorry, I had to get dressed. magnolia 1999
58993.0 I love you. I love you and I'm sick. I'II talk to you....I'll talk to you tommorrow. I'm getting corrective oral surgery tomorrow. For my teeth. For my teeth and for you....for you so we can speak. You have braces. Me too. Me too. I'm getting braces, too. For you. For you, dear Brad. And I don't have any money. And I don't have any money now but I'II get it...I will for you, Brad. I love you, Brad. Brad the Bartender. You wanna love me back? Love me back and I'll be good to you. I'II be god damn good for you. And I won't be mad if you don't know who said what. I won't punish you if you get the answer wrong. I can teach and tell you: Samuel Johnson. :-()-: Brad, honey, you have a special secret crush over here I think, don't take him too lovely -- he might get hurt -- :-()-: You mind your own bussines. :-()-: Gently, son -- :-()-: Brad, I know you don't love me now -- :-()-: "It's a dangerous thing to confuse chidlren with angels..." :-()-: -- and you wanna know the common element for the entire group, like he asks...I'll tell you the answer: I'll tell you, 'cause I had that question. I had that same question....Carbon. In pencil led, it's in the form of graphite and in coal, it's all mixed up with other impurities and in the diamond it's in hard form. "Well...all we were asking was the common element, Donnie...but thank you for all that unnecessary knowledge...ahhh, Kids! Full of useless thoughts, eh?" Thank you. Thank you. And the book says: "We may be through with the past but the past is not through with us." And NO IT'S NOT DANGEROUS TO DO THAT. magnolia 1999
58921.0 You okay? huh? Jimmy? :-()-: And the book says: "We may by through with the past, but the past ain't through with us." :-()-: C'mon, Jimmy, snap up, snap up -- :-()-: In my sleep, Burt. magnolia 1999
59046.0 Willa Cather. :-()-: For 25. Best known for the "tragedy and blood" genre, this author-playwright -- magnolia 1999
59048.0 What were you saying, Stanley? :-()-: I was saying...thinking maybe I'd get my own quiz show someday, Jimmy. Just like you! magnolia 1999
58919.0 You smell like trouble -- :-()-: I'm fuckin' hammered, Burt. :-()-: You ok? :-()-: ooohhhhhh no. magnolia 1999
58959.0 No, what's wrong? :-()-: You mind if I come in, check things? magnolia 1999
59064.0 Hello? :-()-: Hello. Is Claudia here? :-()-: She's asleep. :-()-: Are you her boyfriend? :-()-: You're Jimmy Gator, right? :-()-: Yes. What's your name? :-()-: I'm Bob. :-()-: You're her boyfriend? :-()-: No, I'm just a friend. What are you doing here, I mean...you know Claudia? :-()-: I'm her father. magnolia 1999
59068.0 Do you still have to do homework? :-()-: Not as much as I used to. Ever since we started, I haven't really gone in to school that much because I've been getting more and more auditions -- magnolia 1999
59052.0 I don't think I want that. :-()-: It'll take the pain away. :-()-: It's not really pain. magnolia 1999
59016.0 -- that's right, that's right, and what I'M saying, that none of my competitors can say is this: That there is no need for insight or understanding. Things of the past! Gone, Over, Done. Do you realize how fucking miraculous this is? How fucking razor sharp and cutting edge and ahead of it's time this concept is? I'm talking about eliminating <u>insight</u> and <u>understanding</u> as human values. GOD DAMN I'M GOOD. There is no need for INSIGHT. There is no need for UNDERSTANDING. I have found a way to take <u>any</u> subjective human experience -- in other words -- all the terrible shit or all the great shit that you've had happen to you in your life -- and quickly and easily transform it in the unconscious mind through the subtle and cunning use of language. The "listener-patient" settles into a very light, very delicate, conversationally induced state: NOT A TRANCE, mind you, but a STATE. A state that is brand new. The System's state. What did I do? I REALIZED that concept and put it into practical "get my dick hard and fuck it" use. I'm gonna build a state for the seducer and the seducee to live, vote, breath, pay takes and party 'till dawn. I'm gonna teach methods of language that will help anyone get a piece of ass, tit and tail -- :-()-: Let's talk about -- :-()-: I just realized this is for television, isn't it? I can't swear up and down like I just did. :-()-: It's fine. I can bleep it out. :-()-: I warned you -- I get on a roll... :-()-: -- let's talk more about your background -- :-()-: Muffy -- coffee? magnolia 1999
59023.0 Are you asking me a question? :-()-: Well I guess the question is this: Do you remember Miss Simms? magnolia 1999
58988.0 Who was it that said: "A man of genius has seldom been ruined but by himself." :-()-: -- Samuel Johnson. magnolia 1999
58917.0 Your teeth are straight. :-()-: I need corrective oral surgery. I need the braces. :-()-: Don, you got hit by lightning that time in Tahoe, you went on vacation, I don't think braces is a good idea -- :-()-: I can't believe you're gonna do this to me, the situation I'm in, I don't -- Avi: You know what? Being hit by lighting doesn't matter for getting braces, ok? Now Solomon, let me just ask you once: Please. Please. Don't do this. :-()-: How are you paying tor the braces, Donnie? :-()-: I don't know. magnolia 1999
59001.0 Give me your keys, Don. :-()-: PLEASE DON'T DO THIS! :-()-: GIMME YOUR FUCKIN' KEYS. magnolia 1999
58956.0 ....now that I've met you.... Would you object to never seeing me again? :-()-: What? :-()-: Just say no. :-()-: I won't say, no, wait, Claudia -- magnolia 1999
59061.0 Jimmy, did you touch her? :-()-: I don't know. magnolia 1999
59067.0 I'm sorry. :-()-: It's alright. magnolia 1999
59014.0 All it takes is one second? :-()-: Just one look, one hesitation, one subtle gesture for me to know -- And Bing-Bam-Boom I'm away on a tangent -- I get so fuckin' amped at these seminars and lemme tell you why: Because I Am What I Believe. I am what I teach, I do as I say, I live by these rules as religiously as I preach them: And you wanna know what? I'm gettin' pussy left, right, up, down, center and sideways. :-()-: I'm gonna start rolling -- :-()-: -- go, go, go. I'm givin' pearls here. And I'II tell you samethin' else: I'm not succeding in the bush because I'm Frank TJ Mackey. If anything, there are women out there that want to <u>destroy</u> me -- it makes it twice as hard for me, I run into some little muffin, knows who I am, knows my schemes and plans -- shit, she's gonna wanna fuck around, prove to her friends, say, "Yaddda-yadda-yadda, I saw that guy, he wasn't anything, didn't get me." So me? I'm runnin' on full throttle the whole fuckin' time. Dodging bullets left and right from terrorist blonde beauties. But I'II tell you this: The battle of the bush is being fought and won by Team Mackey. Can I have a cigarette? :-()-: Ok. So, lemme just ask you a couple questions to start -- magnolia 1999
59073.0 They look pretty smart, I think. :-()-: No they don't -- magnolia 1999
59024.0 Hello. Frank. Frank TJ Mackey. :-()-: ...are you Phil...? :-()-: Yeah. I was trying to get in touch with you. We got dissconnected. :-()-: I got your message. That you were trying to get me -- right? :-()-: Yes. I didn't know how to find you. Earl asked me, so I looked through the adress books and there was no number, nothing -- :-()-: Is Linda here? :-()-: She's not here, she went out. I'm sorry. This is all just so, I don't know what, what to do -- your Dad asked me to try and track you down. To get you and I did, I called the number -- Do you wanna come in? :-()-: Yeah let's...maybe just stand. :-()-: These Dogs'll calm down -- you just have to come in -- magnolia 1999
59004.0 How's today then? :-()-: Fuckin' bullshit is what this is. :-()-: Fuckin' bullshit is right, in'it? magnolia 1999
59034.0 --- What was that? :-()-: I didn't hear anything. magnolia 1999
59087.0 What are they gonna do -- beat us? :-()-: Maybe. magnolia 1999
59098.0 What's the problem, what's the problem here? :-()-: I'm fine. nothing. :-()-: Why didn't you answer those questions? :-()-: I didn't know the answer -- :-()-: Bullshit. Bullshit. You know the answer to every goddamn question and I knew the answer to those questions and I'm not half as smart as you are so What Happened? :-()-: I don't know. magnolia 1999
58947.0 I'm gonna run to the bathroom real quick. :-()-: Okey-doke. magnolia 1999
59041.0 Thirty fuckin' years I've been with Rose, don't -- y'know -- with this, and I know what you think -- :-()-: All your other fluzzies? :-()-: Yeah. Yes. :-()-: You're making me feel so dirty and shitty. I feel like a big piece of shit right now. :-()-: Are you gonna tell her what you've done? :-()-: Yes. :-()-: Will you say my name? :-()-: If she asks me any question I want to tell her. I want to tell her everything I've done. :-()-: Well can you do me one favor and don't do that. magnolia 1999
59044.0 Rose is on the phone and here's the cards for today -- :-()-: Fifteen minutes ago, where were those cards? :-()-: I'm sorry. :-()-: I need you to get me Paula -- :-()-: You want her right now? :-()-: Yes. Now. Find her. She's somewhere in the building -- :-()-: We're on the air in twenty minutes, Jimmy. :-()-: Find her, get her and tell her I want to talk to her, Mary. Fucking hell. magnolia 1999
59082.0 Cats and Dogs out there, huh? :-()-: mmmhmm. :-()-: Must have alot goin' on for all that stuff you got back there, eh? You could have quite a party all that stuff.... magnolia 1999
59049.0 We are not on display. I am not a doll. I AM NOT A DOLL...I' M NOT SILLY AND CUTE. I'M SMART SO THAT SHOULDN'T MAKE ME SOMETHING, SOMETHING SO PEOPLE CAN WATCH HOW SILLY IT IS THAT HE'S SMART? I KNOW. I KNOW THINGS. I KNOW. I HAVE TO GO TO THE BATHROOM I HAVE TO GO TO THE BATHROOM AND I HAVE TO GO. :-()-: I'm sorry, Stanley. magnolia 1999
58974.0 DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T GET MAD AT ME -- JUST GIMME YOUR MONEY. :-()-: I'm not mad, son, I will not be mad at you and it's ok and please put it down and I won't be mad and I won't -- :-()-: DAD. magnolia 1999
59042.0 Come and tell me it's over and I'll walk away, Jimmy. I've fucked you behind your wife's back for three years, and you've fucked teenage girls behind mine for the same amount of time -- I'll walk away, you need something for your life, for your conscience, but don't put me in the middle -- :-()-: I won't. :-()-: What happend to you? :-()-: I got in trouble at school. magnolia 1999
59070.0 We're not going out two days before we set the record, it's not gonna happen. :-()-: When they want us done, they'll call in the Harvard S.W.A.T team or some shit. magnolia 1999
59099.0 I didn't I'm fine, I'm fine. :-()-: Stand up. :-()-: I said I'm fine. magnolia 1999
59037.0 This is the LAPD. If anyone is back here I want you to come out and I want you to show yourself to me with your hands in the air -- :-()-: THERE'S NO ONE IN THERE. STAY OUT OF MY MOTHERFUCKIN BEDROOM. magnolia 1999
58962.0 Diet Coke. :-()-: I want a shot of tequila too. :-()-: -- what kind? :-()-: It doesn't matter. magnolia 1999
59055.0 ...No...I don't hate you. Do you want talk...do you really want to talk to me and say things and get things figured out, Jimmy? :-()-: Yeah. :-()-: The question isn't wether or not you cheated on me, the question is how many times have you cheated on me? :-()-: Will that help? :-()-: Yeah. magnolia 1999
59071.0 Do you have an agent, Stanley? :-()-: No. :-()-: You should get one, I'm serious, you could get a lot of stuff out of this -- :-()-: Like what? magnolia 1999
58961.0 Hello. You're back again, huh? :-()-: yeah, yes, hi, hello. :-()-: -- can I get you? :-()-: Diet Coke. magnolia 1999
59095.0 C'mon, man. :-()-: You're late, not me. :-()-: You coulda been in front -- :-()-: -- I didn't see you from the window. magnolia 1999
58963.0 I'm fine. Yes. I'm fine. :-()-: Ready to go,go,go? :-()-: Where's Richard and Julia? :-()-: They're here, they're fine. In the dressing room. See you later -- magnolia 1999
59026.0 How long have you taken care of him? :-()-: For six months. I'm the day nurse... :-()-: Uh-huh. What's going on? :-()-: He's...I'm sorry...so sorry...I've seen this before, you know and you don't.... He's going very fast....Frank...um.... :-()-: Is he in pain? :-()-: I just...he was...but I gave him, I just had to give him a small dose of liquid morphine. He hasn't been able to swallow the morphine pills so we now, I just had to go to the liquid morphine... For the pain, you understand? :-()-: ...uh-huh... magnolia 1999
59029.0 So....Phil....um...I think I'm gonna step in and try and see him and say something if he can...talk...I mean: :-()-: ...ok... :-()-: Can you stand...back...maybe, I mean... just a little bit...in the room is ok, but back from us a little... :-()-: yeah. magnolia 1999
58990.0 Things go round 'n round, don't they? :-()-: Yes they do, they do, but I'll make my dreams come true, you see? I will. :-()-: This sounds Sad as a Weeping Willow. :-()-: I used to be smart but now I'm just stupid. :-()-: Shall we drink to that? magnolia 1999
58941.0 No. :-()-: I got a call of a disturbance, screaming and yelling, loud music. Has there been some screaming and yelling? :-()-: Yes. I had someone come to my door, someone I didn't want here and I told them to leave -- so -- it's no big deal. They left. I'm sorry. :-()-: Was it a boyfriend of yours? :-()-: No. :-()-: You don't have a boyfriend? :-()-: No. :-()-: Who was it? :-()-: I was...he's gone...I mean it's not. It's over, y'know -- magnolia 1999
58950.0 This is my job. :-()-: We were just gettin' warmed up. We were just getting started. :-()-: Well if you listen' to that music too loud again and that fella returns maybe we'll share another cup of coffee -- :-()-: If you're not here for a 422 -- :-()-: No. No. Don't joke about that. That's not funny, Claudia. Please, now. :-()-: I'm sorry. :-()-: Ok, then. Keep your chin up and your music down, alright? :-()-: Yes. I will. It was nice to meet you Officer Jim. :-()-: Just Jim. :-()-: yeah, good, ok. :-()-: Bye, bye, Claudia. :-()-: Good bye. magnolia 1999
59005.0 ...no...no goddamn use. I have a son, y'know? :-()-: You do? :-()-: ...ah... :-()-: Where is he? :-()-: I don't know...I mean, he's around, he's here, in town, y'know, but I don't know...he's a tough one...very.... Do you have a girlfriend, Phil? :-()-: No. :-()-: Get a girlfriend. :-()-: I'm trying. :-()-: And do good things with her...share the thing...all that bullshit is true, y'know...find someone and hold on all that...Where's Linda? :-()-: She went out. She said she went out to run some errands. She'll be back. :-()-: She's a good girl. She's a little nuts, but she's a good girl I think. She's a little daffy. :-()-: She loves you. :-()-: ...ah...maybe...yeah...she's a good one... :-()-: When was the last time you talked to your son? :-()-: ....I dunn...o....maybe ten...five, fuck, fuck....that's another thing that goes -- :-()-: -- memory? :-()-: Time lines, y'know? I remember things but not so -- right there -- y'know? :-()-: Yeah. :-()-: "yeah." the fuck do you know? :-()-: I've seen it before. :-()-: Other fuckin' assholes like me. :-()-: There's no asshole like you. :-()-: ...cocksucker.... :-()-: How come every word you say is either "cocksucker," or "shitballs," or "fuck?" :-()-: Do me a personal favor -- :-()-: Go fuck myself? :-()-: You got it. magnolia 1999
59060.0 She thinks terrible things that somehow got in her head...that I might have done something. She said that to me last time...when it was...ten years ago she walked out the door, "You touched me wrong..." "I know that." Some crazy thought in her, in her head... :-()-: Did you ever touch her? :-()-: ...No.... magnolia 1999
59063.0 You deserve to die alone for what you've done. :-()-: I don't know what I've done. :-()-: Yes you do. :-()-: Stay here, please don't leave me, please, please, if I said I knew would you stay? :-()-: No. :-()-: I don't know what I've done. :-()-: You should know better. magnolia 1999
58920.0 It's been the same fuckin' thing for thirty years, Burt -- :-()-: These adults are tough enough, I think you'll be surprised -- the Mexican's a bit of a question mark -- magnolia 1999
59022.0 And you went to Van Nuys High, right? :-()-: I don't how much I went -- but I was enrolled. I was such a loser back then. I was -- misguided, pathetic -- I was very fat. Not even close to what I am today. Not the Frank TJ Mackey you're eager to talk to because I was swimming in what <u>was</u> as opposed to I <u>wanted</u>. :-()-: Where does that name come from? :-()-: What name? My name? :-()-: It's not your given name, right? :-()-: My mother's name, actually. Good question. You've done you're research. :-()-: And "Frank?" :-()-: "Frank" was my mother's father. :-()-: Ok. That's why. I had trouble locating your school records at Berkely and UCLA. Your name change -- they had no official enrollment -- :-()-: Oh, yeah. No, no, no. They wouldn't -- :-()-: They wouldn't? :-()-: no, no, no. Certainly not. I wasn't officialy enrolled, that's right. Was that unclear? :-()-: Kind of. :-()-: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up there. But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are: Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit. So what we're looking at here is a true rags to riches story and I think that's what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World. Defining It. Controling It and saying: I will take what is mine. You just happen to get a blow job out of it, then hey-what-the-fuck- why-not? he.he.he. magnolia 1999
58964.0 Where's the news department at this studio? :-()-: It's upstairs. :-()-: Have you ever been there? :-()-: Sure, why? :-()-: I'm wondering about the weather department. I'm wonderin' wether or not the weather people use outside meteorlogical services or if they have in-house instruments? :-()-: I can check on that for you, maybe we can take a tour -- :-()-: Ok. magnolia 1999
58957.0 Let me go, leave me, let me go, it's ok, please. :-()-: please, what is it, please -- :-()-: just let me walk out, ok? magnolia 1999
59009.0 ...find him on the...Frank. His name's Frank Mackey -- :-()-: Frank Mackey. That's your son? :-()-: that'snotmy name...find Lily, gimmme that, give it -- magnolia 1999
58940.0 Wilson. :-()-: Ok. Claudia Wilson: You tryin' to go deaf? :-()-: What? :-()-: Did you hear what I said? :-()-: Yeah, but I don't know -- :-()-: -- listenin' to that music so loud: You Tryin' To Damage Your Ears? :-()-: No. :-()-: Well if you keep listenin' to the music that loud you're not only gonna damage your ears but your neighbors ears. :-()-: I didn't realize it was that loud. :-()-: And that could be the sign of a damaged ear drum, you understand? :-()-: Yeah. :-()-: You got the TV on too, keep those on at that same time usually? magnolia 1999
59036.0 WHAT'S THIS? WHAT'S THIS? GOD DAMN BULLSHIT. BULLSHIT. DON'T PUT THOSE -- :-()-: Marcie - CALM DOWN. CALM DOWN and don't do this. I want you to stay -- magnolia 1999
59030.0 I'm here. I'm here now. What do you want? Do you want anything? :-()-: I don't think, he can't... :-()-: ...just wait...Dad...you want something...can you say... magnolia 1999
59031.0 What? What? What now? :-()-: Quietly, slow down, whoa -- :-()-: You can't just come in here. :-()-: The door was open, I got a call -- :-()-: You're just come in -- :-()-: Calm down. :-()-: I am calm. :-()-: I got a call to this apartment, report of a disturbance -- :-()-: There's no disturbance. :-()-: I got a call of a disturbance, you're door was open, I just wanna see what's goin' on -- :-()-: There's no disturbance. :-()-: Then you've got nothin' to worry about. :-()-: You don't tell me, I know my rights, just come right in, you can't -- :-()-: Don't test me, you wanna talk about what the law book says, we can do that, push me far enough and I'll take you to jail -- now calm down. :-()-: I AM CALM. :-()-: You're not calm. You're screamin' and yellin' and I'm here to check on a disturbance that was reported and that's what I'm gonna do - now are you alone in here? :-()-: I don't have to answer your questions. :-()-: No you don't: But I'm gonna ask you one more time: Are you alone in here? :-()-: What does it look like? :-()-: No one else in here? :-()-: You're here. :-()-: OK. That's true. Is anyone else, besides me and besides you in this house? :-()-: No. I said that. :-()-: Are you lyin' to me? :-()-: I live alone. :-()-: Maybe so, but I'm gonna ask you one more time: Is Anyone Else In This House Right Now? :-()-: No I Said. :-()-: Ok. What's your name? :-()-: Marcie. :-()-: Ok. Marice why don't you take a seat for me? :-()-: I preffer to stand. :-()-: I'm not askin', Marcie. magnolia 1999
59092.0 Let's go,let's go, let's go, you shoulda done that ten minutes ago -- :-()-: We need more dog food -- :-()-: -- talk in the car, talk in the car, moves your ass, c'mon -- magnolia 1999
59103.0 Hi. :-()-: Hi. :-()-: ..sorry... :-()-: It's ok. magnolia 1999
58923.0 Hey, Janet, it's Chad. :-()-: What's wrong? :-()-: Nothing's wrong, I just got some guy on the phone on my other line, he's says he works for this guy, this guy who's Frank's father -- :-()-: -- no,no,no what is this? who? What's this guy's name? magnolia 1999
58989.0 It was the lovely Samuel Johnson who also spoke of a fella "Who was not only dull but a cause of dullness in others." :-()-: "The" cause of dullness in others -- :-()-: Picky, picky. :-()-: -- and lemme tell you this: Samuel Johnson never had his life shit on and taken from him and his money stolen -- who took his life and his money? His parents? His mommy and daddy? Make him live this life like this -- "A man of genius" gets shit on as a child and that scars and it hurts and have you ever been hit by lighting? It hurts and it doesn't happen to everyone, it's an electrical charge that finds it's way across the universe and lands in your body and your head -- and as for "ruined but by himself," not if his parents take his friggin' life and his money and tell you to do this and do that and if you don't? well, what -- magnolia 1999
59047.0 I'm gonna need a full name, Stanley. :-()-: Jean Baptiste Poquelin Moliere. magnolia 1999
59006.0 ...I can't hold onto this anymore... :-()-: I'll get you another pain pill. Another morphine pill -- :-()-: ...gimme that fuckin' phone... :-()-: Who are you gonna call? :-()-: I wanna see this...where is he, do you know? :-()-: Who? :-()-: Jack. :-()-: Is Jack your son? magnolia 1999
58935.0 uh...uh...What is it? :-()-: It's the LAPD, can you open the door, please? magnolia 1999
58938.0 Yes. :-()-: No one else in there with you? magnolia 1999
58975.0 I - just - thought - that - I - didn't want - I - didn't - I - didn't - :-()-: It's ok, boy. magnolia 1999
59008.0 This is so boring...so goddamn... and dying wish and all that, old man on a bed...fuck...wants one thing: :-()-: It's ok. magnolia 1999
58968.0 What do you mean, "like what?" -- you could get endorsments and shit -- :-()-: -- Richard. :-()-: Bite it, Cynthia. You could get free things from people that want you to endorse their products. magnolia 1999
59057.0 That's it. :-()-: No one else that I know? :-()-: No. :-()-: How long with Ellen? :-()-: Just once. :-()-: How long with Paula? :-()-: Two years...three years... :-()-: What about now? :-()-: It's over. I talked to her this morning. :-()-: Is it over 'cause you're sick? :-()-: It's over becuase...for all the the right reasons I hope, what I said. :-()-: Do you have any children with anyone? :-()-: What? No, Rose, jesus, no -- :-()-: Well maybe. :-()-: I don't. :-()-: Do you feel better now that you've said this? :-()-: I don't know.... :-()-: I'm not mad. I am, but I'm not. Y'know? :-()-: I love you so much. :-()-: I'm not through asking my questions. magnolia 1999
58927.0 Yeah, hey. Chad. :-()-: Alright, so I'm gonna transfer you over to Frank's assitant, Janet she's gonna see what she can do -- :-()-: Thank you, Chad, and good luck to you and your mother -- :-()-: Thank you. Thank you very much. magnolia 1999
58939.0 For what? :-()-: Ok. For one thing, we're gonna need to turn that music down so we can talk, ok? :-()-: I'm sorry. magnolia 1999
59066.0 Want me to wake her up? :-()-: I'II go....is it...back here? magnolia 1999
58954.0 Wow....huh..."...piss and shit..." :-()-: What? :-()-: You really use strong language. :-()-: I'm sorry -- :-()-: -- no, no, it's fine. Fine. :-()-: I didn't mean...it's seems vulgar or something, I know -- :-()-: It's fine. :-()-: I'm sorry. :-()-: ...nothing. I'm sorry... :-()-: No, I'm sorry. I'm saying I'm sorry. I talk like a jerk sometimes -- :-()-: -- well I'm a real...y'know, straight when it comes to that...curse words I just don't use much -- :-()-: I'm sorry. :-()-: I'm gonna run to the bathroom for a minute...maybe just -- :-()-: ok. :-()-: ok. magnolia 1999
59043.0 Are you ok? :-()-: Fuck no. magnolia 1999
58997.0 What is that? :-()-: Braces. :-()-: Braces? :-()-: Yes. :-()-: You don't need braces. :-()-: Yes I do. :-()-: Your teeth are fine. magnolia 1999
59002.0 -- he's fucking dying, he's dying as we're sitting here and there isn't a fucking thing -- jesus, how can you tell me to calm down? :-()-: I can help you through this the best I know how but there are certain things you are gonna have to be strong about and take care of, now we can go over them, but I need to know that you're listening to me, ok? :-()-: I just, I just -- I just -- I'm just in a fucking state, I know he's going and it's like I don't know how -- just tell me <u>practical</u> things -- What the fuck do I do with his body? What happens when he dies? That next moment: What? What do I do? Then What? :-()-: Well that's what Hospice will take care of for you. They will send a nurse, someone who can take care of all of that for you -- :-()-: He has Phil right now. :-()-: Phil's one of the nurses from the service? :-()-: Yeah. :-()-: If you're happy with Phil taking care of him and helping you, that's fine, but contact Hospice to arrange for the body -- :-()-: -- you don't understand: it's more pain than before and the fucking morphine pills aren't working, he's -- past two days it's like he can't really swallow them and I don't know if they're going down -- I can't see inside his mouth anymore -- I'm up all night staring at him and I don't think the pills are going down and he moans and he hurts -- :-()-: We can fix that, because I can give you -- are you listening? :-()-: I'm listening. I'm getting better. :-()-: Do you wanna sit down? :-()-: I need to sit down. :-()-: Ok. Linda: Earl is not gonna make it. He's dying. He is. He is dying very, very rapidly -- magnolia 1999
58971.0 DADDY! DAD! DAD WHAT THE HELL IS GOIN' ON? :-()-: Stay quiet...stay quiet, son -- :-()-: LET'S GO, LET'S GO, LET'S GET HIS MONEY AND GO -- DID YOU GET HIS MONEY? DID YOU GET IT? DID YOU GET HIS MONEY, DAD? :-()-: No, Son...be quiet...be quiet now... :-()-: C'mon, Dad. We gotta just GET HIS MONEY AND GO, LET'S GO. Let's get the money -- :-()-: We're not gonna do that now. We're not gonna do that now and that's over. :-()-: BULLSHIT. BULLSHIT, DAD WE NEED TO GET HIS MONEY AND GO. magnolia 1999
59080.0 listen...listen to me now, Phil: I'm sorry, sorry I slapped your face. ...because I don't know what I'm doing... ...I don't know how to do this, y'know? You understand? y'know? I...I'm...I do things and I fuck up and I fucked up....forgive me, ok? Can you just... :-()-: ....it's alright.... :-()-: Tell him I'm sorry, ok, yes, you do that, now, I'm sorry, tell him, for all the things I've done...I fucked up and I'm sorry.... And I'm Gonna Turn Away And Walk Now And Not Look At Him Not See My Man, My Earl, I'll leave now...and tell him it's ok and I'm ok. The whole thing was ok with me -- and I know. magnolia 1999
59017.0 I'm confused about your past is the thing. :-()-: Is that still lingering? :-()-: -- just to clarify -- :-()-: So boring, so useless -- :-()-: I would just want to clear some things up: :-()-: Thank you, Muffy. Funny thing is: This is an important element of, "Seduce and Destory:" "Facing the past is an important way in not making progress," that's something I tell my men over and over -- :-()-: This isn't meant -- :-()-: -- and I try and teach the students to ask: What is it in aid of? :-()-: Are you asking me that? :-()-: Yes. :-()-: Well, just trying to figure out who you are, and how you might have become -- :-()-: In aid of what? :-()-: I'm saying, Frank, in trying to figure out who you are -- :-()-: -- there's a lot more important things I'd like to put myself <u>into</u> -- :-()-: It's all important -- :-()-: Not really. :-()-: It's not like I'm trying to attack you -- :-()-: This is how you wanna spend the time, then go, go, go -- you're gonna be surprised at what a waste it is -- "The most useless thing in the world is that which is behind me," Chapter Three -- :-()-: We talked earlier about your mother. And we talked about your father and his death. And I don't want to be challenging or defeatist here, but I have to ask and I would want to clarify something -- something that I understand -- :-()-: I'm not sure I hear a question in there? :-()-: Do you remember a Miss Simms? :-()-: I know alotta women and I'm sure she remembers me. :-()-: She does. From when you were a boy. :-()-: Mm. Hm. :-()-: She lived in Tarzana. :-()-: An old stomping ground --is this the "attack" portion of the interview, I figured this was coming sooner or later -- Is "the girl" coming in for the kill? :-()-: No, this is about getting something right and claryfying one of your answers to an earlier question -- :-()-: Go ahead and waste your time. :-()-: I was told that your mother died. That your mother died when you were young -- :-()-: And that's what you've heard? :-()-: I talked to Miss Simms. Miss Simms was your caretaker and neighbor after your mother died in 1980. magnolia 1999
59045.0 I can't fuckin' do this. :-()-: Are you alright? :-()-: Fuck. I think I'm gonna throw up, I think. I haven't thrown up since I was twenty years old. magnolia 1999
58916.0 What surery? :-()-: Oral surgery. Corrective teeth surgery. magnolia 1999
58985.0 Just throw some money around. Money, money, money. :-()-: This sounds threatening. :-()-: Do you have love in your heart? :-()-: I have love all over. I even have love for you, friend. :-()-: Is it real love? :-()-: Well -- :-()-: -- the kind of love that makes you feel that intagible joy. Pit of your stomach. Like a bucket of acid and nerves running around and making you hurt and happy and all over you're head over heels....? :-()-: Well you lost me with the last couple of cocktail words spoken, m'boy, but I believe it's that sort of love. Sounds nice to me. :-()-: I have love. :-()-: A very chatty-kind, you do, indeed, it seems. :-()-: No. I mean, I'm telling you: I'm telling you that I have love. :-()-: And I'm listening avidly, fellow. :-()-: My name is Donnie Smith and I have lot's of love to give. magnolia 1999
59000.0 I don't know. :-()-: You were gonna ask me weren't you? :-()-: I've been a good worker, Solomon. A hard and loyal -- magnolia 1999
59039.0 What the hell is this Marcie? :-()-: THAT'S NOT MINE. magnolia 1999
58977.0 Ok. Listen. You: c'mere. :-()-: No. :-()-: You wanna disrespect an officer of the law? :-()-: I can help you solve the case, I can tell you who did it. :-()-: Are you a joker? huh? Tellin' jokes? :-()-: I'm a rapper. :-()-: Oh, you're a rapper, huh? You got a record contract? :-()-: Not yet -- "give you the clue for the bust if you show me some trust --" :-()-: Have you ever been to Juvenille Hall? :-()-: I ain't fuckin with you -- :-()-: Hey. Watch the mouth. Watch it. :-()-: C'mon, man, just watch me, watch and listen -- :-()-: Go. Hurry up. Let's go. magnolia 1999
59065.0 Can I come in? :-()-: Yeah. She's sleeping now, I mean -- magnolia 1999
58922.0 Call 911. Call 911 right now. :-()-: No, no, no. I'm fine. It's small, I wanna keep going -- :-()-: no, no, c'mon Jimmy we need to call this quits and you need to see a doctor. :-()-: I'm telling you right now, I'm fine. I lost my goddamn balance and I couldn't see a moment, but I'm ok. :-()-: Call 911, Mary, do it right now. :-()-: You fuckin' don't do that. You don't do it, you cocksucker. I'll fuckin' kill you with my barehands. Go. get the fuck fuck -- we're going back and we finish the show -- :-()-: Jimmy you look like you're about to fuckin' die right here -- :-()-: Shut it. Shut yer fuckin' mouth. magnolia 1999
58994.0 ...please... :-()-: Don't Donnie. Don't do it. magnolia 1999
59105.0 ....you have it...easy....you know? You have a father who loves you, huh? :-()-: Yes. :-()-: You know what it's like to come home scared, scared that maybe if you don't have the money you're supposed to go out each day and get that you're gonna get beaten....by a belt...he hits me with a belt, Stanley.... I'm supposed to sell those candy bars, and if I don't, I come home without the money.... :-()-: ....Why does he do it...? :-()-: Cause he hates me....he hates me so much. :-()-: It's not right. :-()-: I hate it. magnolia 1999
59032.0 I didn't do anything. :-()-: Maybe you didn't, but I'm here to find out about a disturbance. Some neighbors called said they heard screaming and a loud crash. :-()-: I don't know a loud crash. :-()-: And what about screaming? :-()-: I said: I DON'T KNOW. You can't just come in here and start pokin' around -- :-()-: What's this, how did this happen? magnolia 1999
59106.0 I'm sorry to put all this on you, Stanley -- :-()-: I have money. :-()-: ...what...? :-()-: I have money to give you. :-()-: No. No. I have to do this on my own. :-()-: I can take you to get money. I don't need it...I don't <u>need</u> it -- listen to me: I can let you have money so your father won't hit you ever again -- you'll have the money because I don't need it. magnolia 1999
58951.0 I'm sorry, Claudia. :-()-: What is it? Did you forget something? :-()-: No, no. I was wondering...man oh man. I think I feel like a bit of a scum-bucket doing this, considering that I came here as an officer of the law and the situation and all this but I think I'd be a fool if I didn't do something I really want to do which is to ask you on a date. :-()-: You wanna go on a date with me? :-()-: Please, yes. :-()-: Well...is that illegal? :-()-: No. :-()-: Then...I'd like to go...What do you want to do? :-()-: I don't know. I haven't thought about it -- you know what -- that's not true -- I have thought about it. I've thought about going on a date with you since you opened the door. :-()-: Really? :-()-: Yeah. :-()-: I thought you were flirting with me a little. magnolia 1999
58987.0 But you're alright now, so what's the what? :-()-: What? :-()-: That's right. :-()-: I used to be smart but now I'm just stupid. :-()-: Brad, dear? magnolia 1999
59090.0 I don't wanna go, I can't do it this time. :-()-: -- the fuck are you talking about? magnolia 1999
59011.0 She had cancer...from her...in her stomach and I didn't go anywhere with her...and I didn't do a god thing... for her and to help her....shit...this bitch...the beautiful, beautiful bitch with perfect skin and child bearing hips and so soft...her namewasLilysee? He liked her though he did, his mom, Frank/Jack...he took care of her and she died. She didn't stick with him and he thinks and he hates me, ok...see...I'm...that's then what you get? ....are you still walkin' in that car...? :-()-: What? Say it again...walking in the car? :-()-: ....getthat on the tv....there... magnolia 1999
58925.0 Hi, hello, great. This is Seduce and Destroy? :-()-: It is. Can I have your home phone number with area code? :-()-: Well I don't want to order anything, you see. I have a situation, a situation just come up that's really pretty serious and I'm not sure who I should talk to or what I should do but could you maybe put me in touch with the right person if I explain myself? :-()-: I'm really only equipped to take orders -- :-()-: Well can you connect me to someone else? :-()-: Well what's the situation? :-()-: Well, ok. Lemme see how I explain this without it seeming kinda crazy, but here go: I'm, my name is Phil Parma and I work for a man named Earl Partridge -- Mr. Earl Partidge. I'm his nurse. He's a very sick man. He's a dying man and he's sick and he's asked me to help him, to help him find his son -- Hello? Are you there, hello? :-()-: I'm here, I'm listening. :-()-: OK. See: Frank TJ Macky is Earl Partridge's son.... magnolia 1999
58966.0 I have to go to the bathroom, Cynthia. :-()-: Jesus Christ, Stanley, you can't go to the bathroom now. You have exactly one minute before we're back on the air, this is NOT the time to go to the bathroom. :-()-: I'm, I need to go, I'm gonna -- magnolia 1999
59086.0 I don't have regular classes anymore. :-()-: What do you do? :-()-: They just let me have my own study-time, my own reading time in the library. :-()-: That's pretty cool. magnolia 1999
58984.0 You look like you've got money in your pocket. :-()-: Maybe I'm just happy to see my friend, Brad there. magnolia 1999
58995.0 This is so fucked, Solomon. I don't deserve this. :-()-: Don't get strong, Donnie. This is making sense, this making a lot of sense. You are not doing the job, the job I ask you to do, a job I give you. Over and over and over and I'm sorry. But I'm not gonna say I'm sorry that much more. :-()-: Solomon: I am in the middle of so much. So much in my life and this is -- If you do this, if you fire me: I Am Fucked. I can't really explain much, but please, please, I've worked here for four years, four years I've given you and I'm, I'm, I mean what? I'm sorry I was late. I had a car accident. I accidentaly drove into a seven-eleven. It was not my fault. magnolia 1999
59072.0 Commercials, a sitcom, an MOW or something. :-()-: What's MOW? :-()-: Movie Of The Week. I went up for one this morning with Alan Thicke and Corey Haim -- magnolia 1999
59013.0 That's fine. It's nice to meet you. :-()-: Are we gonna tape some stuff now? :-()-: If you're up to it, I've got us set up in a suite upstairs -- :-()-: You got us a room so quick? magnolia 1999
59076.0 I don't want him to die, I didn't love him when we met, and I've done so many bad things to him that he doesn't know, things I want to confess to him, but now I do: I love him. I love him so much and I can't stand -- he's going. :-()-: What kind of medication are you on right now, Linda that's -- :-()-: This is not any fucking medication talking, this isn't -- I don't know. I don't know -- Can you give me nothing? You have power of attorney, can you see him, can you, in this final fucking moment, go see him and make sure --- change the fucking will -- I don't want any money, I couldn't live with myself, this thing I've done -- I've fucking done so many bad things -- I fucked around. I fucked around on him, I fucking cheated on him, Alan. You're his lawyer, our laywer, THERE, I'm his wife, we are married. I broke the conract of marriage, I fucking cheated on him, many times over, I sucked other men's cocks and fuck - fuck - fuck - ....fuck.... Other Things I've Done.. :-()-: Adultery isn't illegal -- it's not something that can be used in a court to discredit the will or -- Linda. Linda. Calm down. :-()-: I can't. :-()-: You don't have to change the will, if what you want to do is get nothing you can renounce the will when it's time. :-()-: Where will the money go? :-()-: Well. Considering that there's no one else mentioned in the will...we'd have to go to the laws of intestacy, which is -- as if someone died without a will -- :-()-: What does that mean? :-()-: The money would go to Frank. The court would put the money in the hands of a relative -- :-()-: -- that can't happen. Earl doesn't want him to have the money, the things. :-()-: -- unless Frank is specifically ommitted as a beneficiary that's what will happen. :-()-: This is so over-the-top and fucked-up I can hardly stand it. :-()-: Linda, you just have to take a moment and breath and one thing at a time -- :-()-: Shut the fuck up. :-()-: I'm trying to help, Linda -- :-()-: Shut the fuck up. Shut the fuck up. :-()-: You need to sober up. :-()-: Now you must really shut the fuck up, please. Shut The Fuck Up. :-()-: Linda -- :-()-: I have to go. magnolia 1999
59040.0 You look great. :-()-: What the fuck is this, Jimmy? :-()-: ...you know... :-()-: Did your wife find out? :-()-: No. :-()-: Then what? :-()-: It's just...too late for me to be fuckin' around. I gotta stop. I gotta clean my brain of all the shit I've done that I shouldn't have done -- :-()-: -- that you shouldn't have done? That you regret, what? This? What's this? Fuck, man, c'mon. Treat me like an asshole, but treat me like an <u>asshole</u>. :-()-: I don't wanna have to lie to anyone. I don't want to hurt anyone else, anymore. magnolia 1999
58928.0 ...what the fuck is this...? :-()-: It's me. Claudia. It's me. magnolia 1999
59025.0 He's in here. :-()-: Let's just wait one minute and stay here, okay? :-()-: Ok. magnolia 1999
58982.0 What happend? :-()-: Doc, just - don't, how close are you? :-()-: I'm about to get off the elevator -- magnolia 1999
59093.0 Cmon,cmon,cmon, that one to? :-()-: I need this one. :-()-: Why the hell do you need all four bags of books to go to school each day? :-()-: I can't carry all of them. I need them. I need my books. I need them to go to school. magnolia 1999
59019.0 Time's up. Thank you for the interview. :-()-: So you sat it out, that's what you did? :-()-: You requested my time and I gave it you, you called me a liar and made accusations. And you say, "If I'd known I wouldn't have asked," then it's not an attack? Well, I don't wanna be the sort of fella who doesn't keep his word. I gave you my time, Bitch. So fuck you now. magnolia 1999
59051.0 How you doing? :-()-: I'm drinking. :-()-: Slowly or quickly? :-()-: As fast as I can. :-()-: Come home soon after the show. :-()-: I went to see her -- some fuckin' asshole answers the door in his underwear, he's fifty years old, there's coke and shit laid out on the table -- :-()-: -- did she talk to you? :-()-: She went crazy. She went crazy, Rose. :-()-: Did you tell her? :-()-: I don't know. I have to go, I don't have time and I have more drinking to do before I go march -- :-()-: I love you. :-()-: Love you too.. :-()-: Bye. magnolia 1999
59091.0 Stanley if you don't fuckin' stand up and go over there I'm gonna beat your ass -- :-()-: I'm sick of being the one, the one who always has to do everything, I don't want to be the one always -- magnolia 1999
58924.0 Hello? :-()-: Ok. Janet you have Phil Parma -- :-()-: Hello, Phil. magnolia 1999
59035.0 No. No. Stay down, Marcie, sit back down on that couch -- :-()-: I don't have to do a god damn thing. magnolia 1999
59010.0 Do you know Lily? Phil..do you know her? :-()-: No. :-()-: ...Lily...? :-()-: No. :-()-: She's my love...my life...love of it... In school when you're 12 years old. In school, in six grade....and I saw her and I didn't go to that school...but we met. And my friend knew her...I would say, "What's that girl?" "How's that Lily?" "Oh, she's a bad girl...she sleeps with guys..." My friend would say this....but then sometime...I went to another school, you see? But then...when high school at the end, what's that? What is that? When you get to the end? :-()-: Graduation? :-()-: No, no, the grade...the grade that you're in? :-()-: Twelve. :-()-: Yeah...So I go to her school for that for grade twelve...and we meet...she was fuckin...like a doll...porcelain doll...and the hips...child bearing hips...y'know that? So beautiful. But I didn't have sex with anyone, you know? I was not...I couldn't do anything...always scared, y'know... she was...she had some boyfriends...they liked her y'know...but I didn't like that. I couldn't get over that I wasn't a man, but she was a woman. Y'see? Y'see I didn't make her feel ok about that....I would say, "How many men you been with?" She told me, I couldn't take it...take that I wasn't a man....because if I hadn't had sex with women...like as many women as she had men...then I was weak...a boy.... But I loved her...you understand? ....well, of course, I wanted to have sex with her...and I did and we were together....we met...age twelve, but then again...age seventeen...something, somethin... I didn't let her forget that I thought she was a bad...a slut.....a slut I would call her and hit her....I hit her for what she did...but we married...Lily and me and we married...but I cheated on her...over and over and over again...because I wanted to be a man and I couldn't let her be a woman...a smart, free person who was something...my mind then, so fuckin' stupid, so fuckin....jesus christ, what would I think...did I think....? ...for what I've done...She's my wife for thirty eight years...I went behind her... over and over...fucking asshole I am that I would go out and fuck and come home and get in her bed and say "I love you..." This'z Jack's mother. His mother Lily...these two that I had and I lost .... and this is the regret that you make...the regret you make is the something that you take...blah...blah...blah... something, something..... Gimme a cigarettee? magnolia 1999
59007.0 You wanna call him on the phone? We can call him, I can dial the phone if you can remember the number -- :-()-: -- it's not him. it's not him. He's the fuckin' asshole...Phil..c'mere... magnolia 1999
59094.0 Be ready at two -- :-()-: Should be one-thirty. :-()-: I got an audition, I won't make it here 'till two, c'mon, I'll see you later. Love you. :-()-: Love you too. magnolia 1999
58992.0 I confuse melancholy and depression sometimes.... :-()-: Mmm.Hmm. :-()-: You see? :-()-: Why don't you run along now friend, your dessert is getting cold. :-()-: I'm sick. :-()-: Stay that way. :-()-: I'm sick and I'm in love. :-()-: You seem the sort of person who confuses the two. :-()-: That's right. That's the first time you're right. I CONFUSE THE TWO AND I DON'T CARE. magnolia 1999
58955.0 Well. :-()-: That felt good to do...to do what I wanted to do. :-()-: Yeah. :-()-: Can I tell you something? :-()-: Yeah, of course. :-()-: I'm really nervous that you're gonna hate me soon. That you're gonna find stuff out about me and you're gonna hate me -- :-()-: -- no, like what, what do you mean? :-()-: You're a police officer. You have so much, so many good things and you seem so together...so all straight and put together without problems. :-()-: I lost my gun. :-()-: What? :-()-: I lost my gun after I left you today and I'm the laughing stock of a lot of people. I wanted to tell you that. I wanted you to know...and it's on my mind and it makes me look like a fool and I feel like a fool and you asked that we should say things, that we should say what we're thinknig and not lie about things and I'll tell you that, this: that I lost my gun and I'm not a good cop...and I'm looked down at...and I know that....and I'm scared that once you find that out you might not like me. :-()-: Oh my god, Jim. Jim, that was so -- :-()-: I'm sorry -- :-()-: That was so great what you just said. :-()-: I haven't been on a date since I was married and that was three years ago....and Claudia...whatever you wanna tell me, whatever you think might scare me, won't...and I will listen...I will be a good listener to you if that's what you want...and you know, you know...I won't judge you.... I can do that sometimes, I know, but I won't...I can...listen to you and you shouldn't be scared of scaring me off or anything that you might think I'll think or on and on and just say it and I'll listen to you.... :-()-: You don't how fuckin' stupid I am. :-()-: It's ok. :-()-: You don't know how crazy I am. :-()-: It's ok. :-()-: I've got troubles. :-()-: I'll take everything at face value. I'll be a good listener to you. :-()-: Ohhhh I started this, didn't I, didn't I, didn't I, fuck. :-()-: Say what you want and you'll see -- :-()-: Wanna kiss me, Jim? :-()-: Yes I do. magnolia 1999
58949.0 --- yeah, yeah, I get in it in my ear. It's TMJ is what it's called technically. :-()-: What's that stand for? :-()-: Tempural-something-mandibular, thing with something, I dunno. But it affects my ear, I don't even know if I have TMJ exactly but just very tight, like - it's like a muscle spasm and it's just gets so clenched -- magnolia 1999
58960.0 You live alone? :-()-: Yes. :-()-: What's your name? :-()-: Claudia. :-()-: Claudia What? magnolia 1999
59069.0 Was it a call back? :-()-: No. But I probably will get a call back. :-()-: If we beat the record, you might get a call back -- :-()-: I'll get it because I'm a good actress, Richard. :-()-: Saucy-saucy. magnolia 1999
58978.0 Presence - with a double ass meaning gifts I bestow, with my riff, and my flow but you don't hear me though think fast, catch me, yo cause I throw what I know with a Resonance - fo'yo'trouble-ass fiend in weenin yo-self off the back of the shelf Jackass crackas, bodystackas dicktootin niggas, masturbatin' yo trigga butcha y'all just fake-ass niggas -- :-()-: -- watch the mouth, homeboy, I don't need to hear that word -- :-()-: -- livin' to get older with a chip on your shoulder 'cept you think you got a grip, cauze you hip gotta holster? Ain't no confessor, so busta, you best just Shut The fuck up, try to listen and learn -- :-()-: Alright, alright, cut it, coolio. That's enough with the mouth and the language. :-()-: I'm almost done. :-()-: Finish it up without the lip. :-()-: Check that ego - come off it - I'm the profit - the proffesor Ima teach you 'bout The Worm, who eventually turned to catch wreck with the neck of a long time oppressor And he's runnin from the devil, but the debt is always gaining And if he's worth being hurt, he's worth bringin' pain in - When the sunshine don't work, the Good Lord bring the rain in. :-()-: Now that shit will help you SOLVE the case. :-()-: Whatever that meant, I'm sure it's real helpful Ice-T. magnolia 1999
59085.0 Pink Dot. :-()-: Hi. I'd like to get an order for delivery. :-()-: Phone number. :-()-: 818-753-0088. :-()-: Partridge? :-()-: Yeah. :-()-: What would you like? :-()-: I'd like to get an order of...um...peanut butter. :-()-: Mmm.Hmmm. :-()-: Cigarettes. Camel Lights. :-()-: mmm.hmm. :-()-: Water. :-()-: Bottled Water? :-()-: Um, no, y'know what? Forget the water, just give me a loaf of bread...white bread. :-()-: Ok. :-()-: And um....do you have Swank magazine? :-()-: Yeah. :-()-: Ok. One of those. Do you have Ram Rod? The magazine, Ram Rod? :-()-: Yeah. :-()-: Ok. One of those. And...um...Barely Legal? :-()-: yeah. :-()-: Do you have that? :-()-: yeah, I said. Is that it? :-()-: That's it. :-()-: Do you still want the peanut butter, bread and cigarettes ? :-()-: Yes. What? Yes. :-()-: Total is $15.29. Thirty minutes or less. :-()-: Thank you. magnolia 1999
59079.0 You don't do that, you don't call him, you don't know to get involved in the bussiness of his, of his of <u>my</u> family. this is the <u>family</u>, <u>me</u> and <u>him</u> do you understand? You understand? NO ONE ELSE. THERE IS NO ONE ELSE. That man, his son does not exist. HE IS DEAD. HE IS DEAD and WHO TOLD YOU TO DO THAT? :-()-: Earl asked me, Linda, please, Linda, I'm sorry -- Earl asked me -- :-()-: BULLSHIT. BULLSHIT HE DIDN'T ASK YOU, HE DOESN'T WANT HIM, HE DOESN'T WANT TO TALK TO HIM, SO FUCK YOU THAT HE ASKED THAT. THERE IS NO ONE BUT ME AND HIM. magnolia 1999
58981.0 I'm walking towards the elevator's, Janet. :-()-: Fine. Phil, you still there? magnolia 1999
59077.0 Let me call you a car, Linda. :-()-: Shut the fuck up. magnolia 1999
58967.0 What happend, what's going on? :-()-: NOTHING. NOTHING HAPPEND. GO AWAY. :-()-: Don't tell me to go away, Stanley. I am the Co-ordinator in this show and you will answer the questions that I ask, you understand? magnolia 1999
59021.0 Where are you from originally? :-()-: Around here. :-()-: the valley? :-()-: Hollywood, mainly. :-()-: And what did your parents do? :-()-: My father worked in televison. My mother -- this is gonna sound silly to you -- she was a librarian. :-()-: Why does that sound silly? :-()-: Well I guess it doesn't. :-()-: Does you mother still work? :-()-: She's retired. :-()-: Are you close? :-()-: She's my mother. :-()-: What does she say about, "Seduce and Destroy." :-()-: "Go Get 'Em, Honey." :-()-: And your father? :-()-: He passed away. :-()-: I'm sorry. :-()-: people die. :-()-: I wouldn'tve asked -- :-()-: Not a problem. :-()-: And you ended up at UC Berkely -- :-()-: From '84 to '89. :-()-: Psychology major? :-()-: Right. :-()-: Do you have your masters? :-()-: ...this close... :-()-: In five years? magnolia 1999
58932.0 Please put your clothes on, please -- :-()-: YOU BURN IN BELL. You burn in hell and you deserve it -- YOU GET THE FUCK OUT. :-()-: Honey. :-()-: GET OUT. magnolia 1999
58933.0 Your mother wants to hear from you -- :-()-: GET THE FUCK OUT OF HERE. magnolia 1999
58934.0 ...Hello...? :-()-: LAPD. Open the door. magnolia 1999
58986.0 ....do you know who I am? :-()-: You're a friend of the family I presume? :-()-: What? What does that mean? :-()-: Nothing special, just a spoke in the wheel. :-()-: You talk in rhymes and riddles and ra...rub-adub --- but that doesn't mean anything to me, see....see...see I used to be smart....I'm Quiz Kid Donnie Smith. I'm Quiz Kid Donnie Smith from the tv -- :-()-: Might of been before my time. magnolia 1999
59101.0 Are we gonna keep going with this game? :-()-: Yes. :-()-: You're two fuckin' days from the record, get through this and I'II do anything for you, you just gotta get through this -- :-()-: Alright. :-()-: hang in there, ok. I love you. magnolia 1999
59059.0 ...say it, Jimmy... :-()-: Do you know the answer to this? :-()-: I'm asking you. I'm asking you if you know why Clauida will not speak to you....please, Jimmy....tell me. :-()-: I think that she thinks I may have molested her. magnolia 1999
58931.0 What the fuck do you want? :-()-: I want to sit. I want to talk to you. :-()-: Don't sit down. :-()-: ...I want to....I want so many things, Claudia. Maybe we can just talk to straighten our things out....there are so many things that I want to tell you -- :-()-: I don't wanna talk to you. :-()-: Please. It doesn't have to be now. Maybe we can make a date to sit down, I didn't mean to walk in on you like this -- :-()-: Why are you here, why are you doing this? Coming in here -- you wanna call me a whore? :-()-: I don't want you to think that I'm that way to you -- I'm not gonna call you a slut or something -- :-()-: Yeah, yeah right -- what the fuck are doing? WHAT THE FUCK ARE YOU DOING IN MY HOUSE? :-()-: Don't yell, honey. Please don't go crazy -- :-()-: I'M NOT CRAZY. Don't you tell me I'm crazy. :-()-: I'm not saying that, I'm sorry -- :-()-: I'M NOT CRAZY. You're the one. You're the one who's wrong. You're the one -- :-()-: I have something, so much -- I'm sick, Claudia. I'm sick. :-()-: Get out of here, get the fuck out of my house -- :-()-: Now STOP IT and LISTEN to me right now. I AM DYING, I GOT SICK...now I fell down and I'm Not...DON'T -- :-()-: GET THE FUCK OUT. :-()-: I'm dying, Claudia. I have cancer. I have cancer and I'm dying, soon. It's metastasized in my bones and I -- :-()-: FUCK YOU. FUCK YOU, YOU GET OUT. :-()-: I'm not lying to you, I'm not -- :-()-: FUCK YOU. YOU GET THE FUCK OUT OF HERE. :-()-: baby, please, please -- :-()-: I'M NOT YOUR BABY, I'M NOT YOUR GIRL. I'm not your fuckin' baby -- magnolia 1999
59084.0 You motherfucker...you motherfucker.... YOU FUCKING ASSHOLE, WHO THE FUCK ARE YOU? WHO THE FUCK DO YOU THINK YOU ARE? :-()-: -- what-what-what, ma'am -- I -- :-()-: I COME IN HERE - YOU DON'T KNOW, YOU DON'T KNOW WHO THE FUCK I AM OR WHAT MY LIFE IS AND YOU HAVE THE FUCKING BALLS, THE <u>INDECENCY</u> TO ASK ME A QUESTION ABOUT MY LIFE -- magnolia 1999
59015.0 -- see, I thought you grew up here in the valley -- :-()-: Like I said, yeah -- magnolia 1999
59054.0 You're my handsome man. :-()-: I'm a bad person. :-()-: No. No. :-()-: No, I mean: I'm telling you this, now. You see? You see I want to make everything clear and clean...and apologize for me....for all the stupid things I've done....that will eat me up.... :-()-: You feel like you want to be forgiven for your sins? Honey, you're not on your death bed, yet....this kinda talk's gonna get you in trouble -- :-()-: --- don't. don't. Please. Just... listen to me...honey.... ...I've done...I've cheated on you. magnolia 1999
59102.0 You have to be nicer to me, Dad. :-()-: Go to bed. :-()-: I think that you have to be nicer to me. :-()-: Go to bed. magnolia 1999
59056.0 How many times....it's ok...just say... Just say... :-()-: I don't even remember...many...twenty... maybe more...not much more...twenty times. :-()-: I don't hate you, Jimmy. But I have a couple questions that I wanna ask.... :-()-: I'll answer anything. :-()-: Was there anyone that I know? :-()-: Yes. :-()-: Who? :-()-: Rose, I don't -- :-()-: hey. :-()-: Paula. Ellen. magnolia 1999
59074.0 You have to go, Stanley. You're the smartest. :-()-: I don't wanna do it. Why can't one of you do it -- magnolia 1999
58929.0 What do you want? Why are you here? :-()-: I'd like to talk to you. Your boyfriend let me in, I just knocked on the door -- :-()-: He's not my boyfriend. magnolia 1999
59012.0 Hello? :-()-: Hi. Is Frank there? :-()-: I think you have the wrong number. :-()-: I'm looking for Frank Mackey. :-()-: No. :-()-: Is this 509-9027? :-()-: Yeah. You have the wrong number. There's no one named Frank here. :-()-: Alright. Thank you. :-()-: Yep. magnolia 1999
59053.0 How do we do this, then? :-()-: We just do it...we do it and we figure it out and we do as we do, I guess... :-()-: Do you love me, Rose? magnolia 1999
59038.0 This is the LAPD, if anyone is in the closet I want you to come out and show yourself to me, slowly and with your hands up -- :-()-: THERE'S NO ONE IN THERE! :-()-: Marcie - quiet down! Now if anyone is in the closet, come out now -- :-()-: THERE'S NO ONE IN MY MOTHERFUCKIN CLOSET AND STAY OUT OF MY BEDROOM, STAY OUT OF MY GOD DAMN BEDROOM. :-()-: -- do not do this -- my gun is drawn and If I Have To Open That Closet you will get shot -- Step Out Now. magnolia 1999
58918.0 No need for braces, Donnie. :-()-: THAT'S NONE OF YOUR BUSSINESS. I HAVE BEEN A GOOD WORKER, A GOOD AND LOYAL WORKER FOR YOU, YOU FUCKING ASSHOLE. :-()-: HEY FUCK YOU DON WATCH IT NOW. magnolia 1999
58996.0 I don't have any money, Solomon. If you fire me -- :-()-: -- I give you money, I give you a paycheck. Your sales suck, Don. I give, I give. When I find you, when I meet you, what? I put you on the billboard, I put you in the store, my salesman, my fucking representation of Solomon and Solomon Electronic, Quiz Kid Donnie Smith from the game show -- :-()-: I lent my name, my celebrity. Exactly -- :-()-: FUCK YOU. I pay you, I paid you. I give you a fucking chance and a chance and over and over, over you let me down. I trust you with so much. The keys to my store, the codes to my locks, the life, the blood of my bussiness and return is smashing in seven-eleven, late, <u>always</u> late, loans -- I loaned you money for your kitchen that you never did -- :-()-: I paid you back. :-()-: Two years! Two years later and out of your paycheck, I never charge interest -- :-()-: Solomon, please. Please. I am so fucked here if you do this. This is the worst timing. The worst timing I could ever imagine. I need to keep working. I have so many debts, so many things, I have, I have, I have -- I have surgery -- I have my oral surgery coming -- magnolia 1999
59003.0 This is the number for Hospice. Ok. Now. As far as the morphine pills go, there is something else to consider that can take the pain away that he is in, there is a very strong and very potent solution of liquid morphine....it's a little bottle, with an eye dropper and it's easy to get in his mouth and drop on his tounge and it will certainly diminish the pain that he is in but you have to realize that once you give it to him; there really is no coming back, I mean, it will certainly cure his pain, but he will float in and out of consciousness, even worse than he is now, Linda. I mean, any sign of the recognizable Earl will pretty much go away -- :-()-: -- how the fuck can I say anything to that -- I don't know what to say to that -- :-()-: The job here is to make him as comfortable as possible -- right now -- our job is to just try and make it as painless as possible. Right? You understand? magnolia 1999
59100.0 ...oh Jesus, what the fuck...? :-()-: I'm fine. I'm fine, I just wanna keep playing -- :-()-: Why did you do this? magnolia 1999
58946.0 Is this boyfriend bothering you? :-()-: I don't have a boyfriend. :-()-: The gentleman who came to the door -- :-()-: -- is not my boyfriend. :-()-: Many times, in damestic abuse situations the young lady is afraid to speak, but I have to tell you that, being a police officer, I've seen it happen: Young woman afraid to speak, next thing you know, I'm gettin' a call on the radio, I got a 422 -- :-()-: It's not -- what's a 422? :-()-: It's where situations like these lead, Claudia, unless you do something about it early, if and when the police call and come for help. Now there are certain measures you can take -- :-()-: It's not my boyfriend -- and it's not anything -- it's over. Really. It's not. He won't came back. :-()-: I don't wanna have to come back here in an hour and find that there's been another disturbance. :-()-: You won't. You won't have to. :-()-: But I wouldn't mind comin' back in an hour just to see your pretty face! magnolia 1999
58944.0 got some coffee brewing, huh? :-()-: Yeah...it's not...it's been on for a bit -- :-()-: I like iced coffee, generally, but a day like this, rain and what not, I enjoy a warm cup -- :-()-: -- do you wanna cup? :-()-: That's great, thank you. magnolia 1999
58980.0 Doc it's Janet. :-()-: What's up? :-()-: I have to talk to Frank, is he nearby? :-()-: He's doing the interview with the lady -- :-()-: I need you to interupt him, I need to get him on the phone with me right away -- :-()-: What happend? :-()-: Doc, go get Frank and put him on the phone. magnolia 1999
58983.0 Phil, hang in just one more minute ok? I'm gonna put you on hold -- Doc you still there? :-()-: Yeah, I'm here, I'm off the elevator, walking down the hall, now -- magnolia 1999
59096.0 You ready to keep winning? :-()-: Sure. magnolia 1999
59081.0 Hello. :-()-: Hi. magnolia 1999
58965.0 C'mon guys, settle down -- :-()-: Cynthia? :-()-: What? :-()-: How much time do we have? :-()-: Not enough, what do you want? :-()-: I should maybe go to the bathroom. :-()-: Can you hold it? :-()-: I don't know. :-()-: Just hold it, you'll be fine. magnolia 1999
58942.0 You mind if I check things back here? :-()-: It's fine. magnolia 1999
58945.0 I don't know how fresh it's gonna be -- :-()-: Oh, it'll be fine, I'm sure, Claudia. :-()-: You take cream or sugar? :-()-: That'd be fine. So, Claudia, lemme just say, so I can get my role of LAPD officer out of the way before we enjoy our coffee I'm not gonna write you up or anything, I'm not gonna give you a citation here -- but the real problem we have is that there are people around here, people that work from their homes, people tryin' to get some work done, and if you're listenin' to your music that loud: They're incovenienced by that. If you had a job you'd probably understand, but I see you like listenin' to your music and that's fine, you're just gonna wanna keep it down at a certain volume, maybe memorize what number you see on the dial and just always put it to that -- If it's the middle of the day -- that's what I do -- just put it on two and a half and that's a good listening level, alright? I see you like listenin' to your music loud, but, hey, forget about the neighbors, you end up damaging your own ears ok? :-()-: Yeah. :-()-: Arlight, then. Cheers. magnolia 1999
58948.0 Ok, ok. I'm back. :-()-: This is, for not a fresh cup, a great cup of coffee, Claudia -- :-()-: Thank you. magnolia 1999
58958.0 -- you the resident here? :-()-: Yes. :-()-: You alone in there? magnolia 1999
59104.0 Hi. :-()-: Hi. :-()-: ..sorry... :-()-: It's ok. magnolia 1999
59020.0 You're hurting a lot of people, Frank -- :-()-: -- fuck you. magnolia 1999
58976.0 How much you pay me for my help? :-()-: I think it's more complicated than that little man. :-()-: Put me on the payroll, find out, find out wassup -- :-()-: You don't just sign up to be a police officer -- it's about three years of training -- ok? :-()-: I'm trained, I'm ready to go, you wanna buy some candy to help underprivelaged youth in the -- :-()-: Sorry, little man. :-()-: You wanna take my statement, I'll perform for you, gotta get paid though, gotta get PAID. :-()-: Why the hell aren't you in school? :-()-: No school today. My teacher got sick. :-()-: They don't have substitute teachers where you go to school? :-()-: Nope. So what'd they find out in there? :-()-: That's confidential information, little man. :-()-: Tell me what you know, I'll tell you what I know -- :-()-: No Can Do. :-()-: Leave this one to the detectives, they ain't gonna solve shit, I can help you, make you the man with a plan, give you the gift that I flow -- think fast -- you wanna know who killed that guy? magnolia 1999
58953.0 Did you ever go out with someone and just....lie....question after question, maybe you're trying to make yourself look cool or better than you are or whatever, or smarter or cooler and you just -- not really lie, but maybe you just don't say everything -- :-()-: Well, that's a natural thing, two people go out on a date, something. They want to impress people, the other person...or they're scared maybe what they say will make the other person not like them -- :-()-: So you've done it -- :-()-: Well I don't go out very much. :-()-: Why not? :-()-: I've never found someone really that I think I would like to go out with. :-()-: And I bet you say that to all the girls -- :-()-: No, no. :-()-: You wanna make a deal with me? :-()-: ok. :-()-: What I just said...y'know, people afraid to say things....no guts to say the things that they...that are real or something... :-()-: ...yeah... :-()-: To not do that. To not do that that we've maybe done -- before -- :-()-: Let's make a deal. :-()-: Ok. I'll tell you everything and you tell me everything and maybe we can get through all the piss and shit and lies that kill other people.... magnolia 1999
59018.0 C'mon, Frank. What are you doing? :-()-: What am I doing? :-()-: Yeah. :-()-: I'm quietly judging you. magnolia 1999
58972.0 GIVE US YOUR MONEY MAN. :-()-: Son, don't -- :-()-: BULLSHIT, BULLSHIT DAD WE GOTTA GET HIS MONEY -- :-()-: -- no. :-()-: GIVE US YOUR MONEY. :-()-: Put the gun down, please, boy. :-()-: GIVE US YOUR MONEY, KID. :-()-: Son, please, now.... :-()-: DAD -- :-()-: Please, boy, put it down and it's ok. magnolia 1999
59083.0 You been on Prozac long? Dexadrine? :-()-: ...I don't.... magnolia 1999
59027.0 How long...you think? :-()-: Um...soon tonight...I think, yes? Tommorrow...I mean...very soon...very... :-()-: When did he go off chemo? :-()-: About three weeks ago. :-()-: .....have you ever seen this..I mean, never mind, you said -- :-()-: I work as a nurse, for a proffesion -- :-()-: Uh. huh. :-()-: I'm really sorry. :-()-: He's in here --? :-()-: Yeah. magnolia 1999
59033.0 You just woke up. And what'd you have a party last night, the way this place looks? :-()-: I went out last night. :-()-: Ok. Marcie. Starting now I want you to have a new attitude with me. The more you play games, the more suspicious I'm gonna become that you've been up to something. :-()-: It's a free country, you can think anything you want. :-()-: Yes I can, Marcie. And until you start givin' me some straight answers: I'm gonna assume that some mischief has been goin' on here. :-()-: Mischief? What the fuck you talkin' about, mischief? :-()-: Bad and illegal behavior. That's what I mean. Ok? Mischief. Now have you been doin' some drugs today? :-()-: No. :-()-: You on any medication? :-()-: No. :-()-: Been drinkin' today? :-()-: It's ten o'clock in the morning -- magnolia 1999
59088.0 The fuck is wrong with you? :-()-: I gotta go to the bathroom. magnolia 1999
59107.0 What is that? :-()-: It's frogs. It's raining frogs. :-()-: ...fuck you mean, it's raining frogs? :-()-: It's raining frogs from the sky. :-()-: ....what the fuck, what the fuck.... :-()-: This happens....this is something that happens. :-()-: What the fuck is goin' on, WHAT THE FUCK IS GOING ON? magnolia 1999
59075.0 You don't want any water? :-()-: No...I just... I'm so fucked up here Alan, I don't know...there's so much...so many things -- :-()-: Are you on drugs right now? :-()-: If I talk to you...y'know...if I tell you things...then you're a lawyer, right? You can't say things, you can't tell anyone, it's like the privelage, right? Attornery-client, you understand? :-()-: Not exactly, Linda. I'm not sure where you're going with this -- :-()-: Like a shrink, like if I go to see a shrink, I'm protected, I can say things -- fuck -- I don't know what I'm doing -- :-()-: Linda, you're safe. Ok. It's alright. You're my friend. You and Earl are my clients and what you need to talk about won't leave this room, you have something you have to say -- :-()-: -- I have something to tell you. I have to tell you something. I want to change his will, can I change his will?...I need to --- :-()-: You can't change his will. Only Earl can change his will. :-()-: No, no....no, you see...I never loved him. I never loved him, Earl. When I started, when I met him, I met him and I fucked him and I married him because I wanted his money, do you understand? I'm telling you this now...this I've never told anyone...I didn't love him. And now....I know I'm in that will, I know, I was there with him, we were all there together when we made that fucking thing and all the money I'II get -- I don't want it -- Because I love him so much now...I've fallen in love with him now, for real, as he's dying, and I look at him and he's about to go, Alan, he's dead...he's moments... I took care of him through this, Alan. And What Now Then? magnolia 1999
58969.0 Let's go, c'mon, get up -- :-()-: Did we win or lose, I mean --? :-()-: I don't know, Richard, they need to talk it over -- magnolia 1999
58979.0 Did you listen to me? :-()-: I was listening -- :-()-: -- I told you who did it and you're not listening to me. :-()-: -- and I'm through playin' games. magnolia 1999
59089.0 Did you piss your fuckin' pants, Stanley? :-()-: Shut up -- shut up -- magnolia 1999
58952.0 Do you wanna go tonight? I mean, are you working? :-()-: No, I'm off tonight. I would lov-like, to go tonight, I can pick you up, I can pick you up here at about what time? What time? :-()-: Eight o'clock? :-()-: What about ten o'clock, is that too late? I don't get off and then -- :-()-: Oh sure yes, that's fine, late dinners are good. Should I get dressed up or -- ? :-()-: No, no, just casual maybe, maybe I thought -- there's a spot I like to go, it's real nice that overlooks a golf course and the course is lit up at night -- :-()-: Billingsley's? :-()-: Yeah, You know it? You know Billingsley's? :-()-: It's my favorite place -- :-()-: Oh, see? This is great. Ten o'clock. :-()-: Great, bye. :-()-: Bye. magnolia 1999
58943.0 What are you lookin' for? :-()-: Claudia: Why don't you let me handle the questions and you handle the answers, ok? :-()-: ok. :-()-: You just move in here? :-()-: About two years ago. :-()-: Bit messy. :-()-: Yeah. :-()-: I'm a bit of a slob myself. :-()-: Yeah. :-()-: You and your boyfriend have a party last night? :-()-: I don't have a boyfriend. magnolia 1999
58936.0 OPEN THE DOOR. :-()-: I'm coming! magnolia 1999
58973.0 It's ok -- :-()-: We gotta get his money so we can get outta here -- we gotta -- :-()-: That idea is over now. We're not gonna do that now. magnolia 1999
59028.0 I've heard your tapes on the phone. :-()-: Oh yeah. :-()-: When they put me on hold, to talk to you...they play the tapes. I mean: I'd seen the commercials and heard about you, but I'd never heard the tapes .... :-()-: Uh. huh. :-()-: It's interesting. :-()-: Mmm. magnolia 1999
59050.0 I don't mean to cry, I'm sorry. :-()-: It's okay, Stanley. It's alright. magnolia 1999
58999.0 I've been a good worker -- :-()-: Don't do this, Don. magnolia 1999
59058.0 Why doesn't Claudia talk to you, Jimmy? :-()-: Why, well I think we've, we both don't know...what do you mean? :-()-: I think that you know. :-()-: Maybe...I don't... magnolia 1999
58991.0 "If a brick weighs one pound plus one half brick -- how much does the brick weigh?" "Well if subtracting the half of brick from the whole brick you got one half of brick, equals one pound so therefore the brick equals two pounds --" "A little more than kin and less than kind," is Hamlet to Claudius. "The sins of the father laid upon the children," is Merchant of Venice but borrowed from Exodus 20:5 and "win her with gifts if she respects not words," is Two Gentleman from Verona. Where? Who? How and Why, Kids? :-()-: "Why don't you shut the fuck up," is me to you, Chapter Right Here, Verse Right Now. magnolia 1999
58930.0 Wanna call me a slut now, something? :-()-: No. No. magnolia 1999
59097.0 Go to it, handsome. :-()-: See you. magnolia 1999
58998.0 And how much is braces? :-()-: It's...doesn't matter.... magnolia 1999
82825.0 Dude, what are you doing here? :-()-: You killed me, remember? Now I have to hide out here with you. Where have you been? xxx 2002
82807.0 Nothing. He had two days. :-()-: Perhaps he is not all he says he is. xxx 2002
82771.0 We're on to the game, ese. You got the fake blood splattered all over the walls, you got your torture tools... It's all very cute. But come on, let's quit while you're ahead. I'm only trying to save you a beatin'. :-()-: You saving me? You talking pretty tough for a guy got himself chained to the ceiling. :-()-: Alright. You wanna eat through a tube, be my guest. :-()-: Now you're gonna make me enjoy this, funny guy. Now I'm gonna take the whole foot off. Whatchu think of that? xxx 2002
82838.0 Don't look at me, X, she handles all the details. :-()-: It'll work faster if I have an account number. xxx 2002
82796.0 Don't you see, X? For the betterment of humanity, he's going to start World War Three. :-()-: He's a regular humanitarian. And all this time I thought he was just a tool. xxx 2002
82765.0 The usual prospects. Convicts, contract killers, murde... :-()-: The scum of the Earth. :-()-: But programmable. And expendable. :-()-: I've seen enough. Turn it off. xxx 2002
82751.0 You've really got me confused, Cage. On the one hand you showed leadership, courage under fire, a willingness to protect men you hardly knew... and on the other you have an arrest record that pegs you as near sociopathic. Help me out here. I'm not following your evolution. :-()-: You want the cheap backstory? The runaway mom, the suicide dad and the foster homes? Gimme a break. You're not interested in my past, you're interested in my future as some kind of spy. :-()-: You're perceptive too. I forgot to add that to the list of surprises. I'm with the National Security Agency. And unlikely as it may sound, I need your help. :-()-: I'm not interested. I've already got a job. :-()-: You're an adrenaline junkie with one foot in the penitentiary. You risk your ass building a daredevil myth that means nothing and you're not getting any younger. :-()-: I plan on getting a lot older. And playing spy games sounds like a quick way to get yourself dead. :-()-: That's too bad. I thought a guy like you would appreciate the challenge. xxx 2002
82799.0 Where's he headed? :-()-: He owns a big industrial complex on the river. He'll launch it from there. xxx 2002
82803.0 He's shut out the communication circuit! :-()-: You can't talk to it? :-()-: No, it's like a safety. We are going to have to access the manual controls. :-()-: A manual control on a moving torpedo. This day keeps getting better and better. xxx 2002
82811.0 Hate those Russkie choppers. Rattle- trap pieces of garbage. I'm agent Shavers. Toby Lee Shavers. I'm looking for what's his name. Three X' s. :-()-: That's would be me. xxx 2002
82795.0 They left. To a fortress in the mountains. :-()-: We'll have to go after them. :-()-: Aren't you afraid? :-()-: A long time ago I learned how to put fear away in a little place in my head. Maybe that's my problem. If you don't feel fear you can do some amazing things, but you can also screw your life up in ways you can't imagine. :-()-: I must be healthy, then, because I'm scared to death. I guess I'd better get back to Yorgi. :-()-: You don't have to do that. :-()-: Yes, I do. You're right. I can't just let him go free. Besides, I can help you from the inside. :-()-: Aren't they gonna wonder were you've been? :-()-: I was supposed to wait for the cop to kill you and then kill the cop. They'll believe me. I've been lying for so long, one more time won't hurt. :-()-: When this is over, we're just gonna take off, the two of us. Take a vacation. :-()-: What the hell are we doing? We're doing all this together, trusting each other, making plans, and we haven't even slept together yet. xxx 2002
82783.0 Bullshit. I see you look at everything, study everything, ask questions. I know exactly what you're doing. Empty your pockets. :-()-: You've got to relax, baby, you're paranoid. xxx 2002
82756.0 Up yours. :-()-: Crude and defiant as always, Triple X. It fits so well because you're obscene. xxx 2002
82792.0 Are you alright? :-()-: What the hell are you doing? You could've killed me! :-()-: I heard you talking. I could tell where you were in the room. :-()-: I don't know what's going on anymore. I thought I was square with that dude. Now everybody's switching sides like it's the WWF. xxx 2002
82763.0 You know what they say, the only way to change the system is from the inside. :-()-: Oh yeah? Why don't we start by taking this thing off my ankle? The poison needles? :-()-: On come on, X. Did you really think we were that barbaric? :-()-: No needles, huh? What about the acid bath? :-()-: All for show. Just a psychological mind game to get you to do the right thing. :-()-: You're a jerk-off, you know that? xxx 2002
82817.0 Now that I've given you the overview, we're going to spend the next couple hours going into extensive operational detail. :-()-: I thought that was in detail. xxx 2002
82845.0 Screw the world. If I'm gonna die for something, it better be bitches and money. :-()-: That's what I'm talking about, man! That's why Anarchy 99 was born. xxx 2002
82750.0 Congratulations, you've just graduated at the head of your class. :-()-: You're a cold piece of work. You almost got three people killed out there. :-()-: Good thing you were there to save the day. Come walk with me. xxx 2002
82787.0 That's how you eat? It's like a gerbil. :-()-: I'm from L.A. :-()-: So why don't you tell me something about who you gather information for. They obviously have very deep pockets. Are you from a rival clan? :-()-: Let's just say I'm freelancing and leave it at that. :-()-: Whoever sent you knew what they were doing. You and Yorgi are practically twins. Two nihilistic fashion victims who make a lot of noise but don't say much. :-()-: Don't judge a comic book by it's cover. :-()-: You agree with a lot of what he says, don't you. :-()-: I know where he's coming from. The only thing I really don't get is why he dumped you. Up until I found that out, I thought his judgment was pretty sound. :-()-: Don't even start. I'll be honest, I don't trust you. Bur lets pretend for a minute that what you say is true, that I want to leave. What can you do about it? :-()-: The people I know want facts, the kinds of things an insider would know. They're interested in putting Anarchy 99 out of business. :-()-: All I have to do is risk my life providing you with these facts. :-()-: We could work together. Then maybe we could both get the hell out of here. There's a beach in Bora Bora with my name on it. :-()-: A beach named X? That I'd like to see. :-()-: So there you have it. I guess you just have to ask yourself, how bad to you want out? xxx 2002
82789.0 I can't let this happen. I want you to go out the back. I'll make up some excuse. :-()-: They'll kill you, you know they will. Besides, there is no back door. Keep smiling. Ready? Now. xxx 2002
82852.0 You both could have been a part of it. You could have ridden with us into a new day, the dawning of a new age of -- :-()-: Oh just shoot us already. I'm sorry I asked. xxx 2002
82810.0 The Peace conference... :-()-: Nice place to start, don't you think? xxx 2002
82773.0 Next we got a Siberian redneck, Viktor. Into snowmobiles and snowboarding. So naturally, he covers prostitution and the drug trade. :-()-: Naturally. xxx 2002
82840.0 I had to inspect the merchandise first. :-()-: Of course. xxx 2002
82822.0 My name is Slovo, Czech secret police. When you are here you are under my jurisdiction, you take my orders, you do what I say. If you become any kind of an inconvenience, I'll shoot you. :-()-: Here we go again. :-()-: You're here because your government is putting pressure on my government. This is an internal affair, a Czech affair, that you are interfering with. I will warn you once: Don't shit in my lawn. Get whatever information your government seeks and get our. :-()-: First of all, you should kill whoever sold you that suit. Two, I don't wanna be here either, so just step off. Three, if you had the authority to shoot me you would've done it by now, so just ease up on the machismo, bitch. Now that we've got that sorted out, I'm gonna get some rest. xxx 2002
82843.0 I don't believe it! You can't shoot a cop in the middle of the street! :-()-: Had to do what I had to do. xxx 2002
82784.0 Hey, you're good. :-()-: Maybe we should start again, yes? I know so little about you, except that you're not what you say you are. :-()-: That makes two of us then, sweetheart, because I ain't buying your bullshit either. You're not like them, I can see it in your eyes. So you tell me, who's bullshitting who? xxx 2002
82834.0 Cops. Like a plague. No matter how many you pay there's always another with his hand out. How did you pick him out? :-()-: He flashed his badge to half the bar when he bought his drink. :-()-: I appreciate you bringing this to our attention. Whatever you want, the rest of the night, consider it on the house. xxx 2002
82831.0 Help me with this guy. :-()-: It's like you said, every man for himself. xxx 2002
82837.0 We seen all your shit! So you want cars? We get whatever cars you need. What are you looking for? :-()-: Ferrari's, Lamborghini's... high end pasta rockets. Ten to start. xxx 2002
82766.0 They're degenerates. There's not a man in there that would give a damn if the Chinese took over. :-()-: That's exactly why we need them. xxx 2002
82785.0 The eyes don't lie. All this has gotten to you, hasn't it, Petra? You came in as Yorgi's girlfriend and you stayed because it was fun. Now you don't like it so much, but you're in so deep you can't get out anymore. Tell me if I'm wrong, Petra. :-()-: Go to hell! :-()-: Look at you. You're helping run things now. You're a gangster. I bet that snuck up on you. You woke up one day and you were a criminal. :-()-: I don't know what you're talking about. xxx 2002
82809.0 What was that for? :-()-: I'd forgotten how exciting it is working together. :-()-: You've been so quiet lately, Petra, I thought you no longer cared. :-()-: Why don't we go below decks and work off some of this adrenaline. :-()-: Viktor! Stay the course. xxx 2002
82782.0 Who are you? :-()-: We hung out last night, remember? :-()-: I also remember you drove your car here. Who are you really? Make no mistake, I will shoot you and not feel bad about it. Who are you working for? :-()-: Hey, take it easy. I'm just a dude trying to make a buck. xxx 2002
82804.0 It's running three meters down. Twenty knots. :-()-: It has to surface to release the nerve agent, right? :-()-: By then it's too late! The only way to safely dispose of the nerve agent is in deep water! The chemicals will break down and dissipate! :-()-: Use your grenades! Maybe we can blow it up underwater! xxx 2002
82781.0 Come on, don't front like that. You'll put a guy right off you. :-()-: Don't even bother, X man, I'm not your type. :-()-: That right? Why's that? xxx 2002
82776.0 Just about anything I want, it looks like. This is gonna be tough, though. There's no way to save this game. I gotta get it right the first time through. :-()-: What do you get if you finish? :-()-: Nothing, really. My guy is just doing it to stay alive. :-()-: Oh. I bet by the end he gets something out of it. :-()-: Like what? :-()-: He gets to be the hero. xxx 2002
82820.0 What...? :-()-: I was gonna wait till the islands, but... :-()-: You're serious? :-()-: Of course I'm serious. I bought it, didn't I? :-()-: This is so typical. I can't believe you. I've barely seen you for the last three weeks and now this? Are you out of your mind? :-()-: I don't know. I thought this is what you wanted. You want stability, here it is. :-()-: You can't just propose to me out of nowhere. You think that's going to solve our problems? I'm sorry, X. It was a kick for a while, but it's over. You're just not going anywhere. :-()-: You're not exactly "going anywhere" yourself. :-()-: You're wrong about that. I'm heading out that door right now. xxx 2002
82772.0 What game is that? :-()-: Slick graphics, huh? See these dudes? They're called "Anarchy 99", they're the bad guy bosses. :-()-: Do they have any special powers? The bosses always have special powers. :-()-: You wanna check it out? Come here. xxx 2002
82761.0 Snow covered fortress. Army of bad guys. The usual. :-()-: I'm on the way with a team to relieve you. What's the latest? :-()-: They retrofitted Ahab with some kind of rocket launcher. They were loading canisters of liquid into it, light colored and dark colored in the same tube but separate. Something tells me this it that "classified" stuff you didn't want me to know about. Does the song "Silent Night" mean anything to you? :-()-: Yes it does. :-()-: Come on, Gibbons, you can do better then that. :-()-: Silent Night is the name of a top secret binary nerve agent. The glass canister is shot into the air and detonated. The black and white chemicals mix, forming a toxic cloud. When it settles down to Earth, it'll kill everything in the vicinity. :-()-: Jesus Christ, Gibbons, this is something we came up with? :-()-: Yeah, that's right. So now that you know, you understand why it's important that we get it back. :-()-: Kinda funny, though, isn't it? We're not supposed to be making weapons like that anyway. Guess we shoulda played by the rules. :-()-: Somebody else makes those decisions, not guys like you and me. You've done your job, X, head back to Prague. I'll be landing in 90 minutes. This is my operation now. :-()-: We don't have that kinda time, boss, I don't think Prague's gonna be around much longer. I've gotta go now. I've got a lot of bad guys to kill. xxx 2002
82778.0 Do you know what a wire transfer is? :-()-: Is she for real? Honey, maybe you should quiet down and let the grownups have a conversation. :-()-: My goodness, a word with four syllables. I should get some ice before your brain gets too hot. :-()-: Sure. Just chisel some off your heart. :-()-: So cute. He shows up for a battle of wits with a mental butter knife. xxx 2002
82768.0 Alright, do what you want. But keep the screws on him. He's a wild card. That could be either good or bad. :-()-: So the odds are up to fifty-fifty? I can deal with that. xxx 2002
82806.0 What are you doing? :-()-: It's about your next mission. You need to be debriefed. xxx 2002
82836.0 I remember that one where you jump the motorcycle over the freeway at rush hour. :-()-: When else are you gonna do it? xxx 2002
82760.0 Thanks. :-()-: You're a grungy little phoenix, you know that? Keep up the good work. :-()-: You ain't seen nothing yet. xxx 2002
82828.0 What's so damn funny? :-()-: Been to any good diners lately? xxx 2002
82798.0 Did you do your homework? :-()-: If they launch it, I know how to bring it back. :-()-: Let's do this. xxx 2002
82827.0 Sorry, "dude". :-()-: What's the deal? :-()-: Oh come on, don't feel bad. You got played, so what? You're new at this. Did you think you could just stroll into this business one day and have all the angles figured out? :-()-: Why you dogging me up? :-()-: They came and found me. They said they'd kill me unless I help them. Plus they offer me a lot of money. No big deal. These things happen. Dump your gun on the floor. xxx 2002
82847.0 You got a great set-up here Yorgi. You really know how to live. :-()-: It's a beautiful town, Prague. It's been good to me. :-()-: I've been here before, when I was a kid. My old man was in the service, we used to live on the Army base in Hamburg. :-()-: You, an Army brat? I don't see that one at all. Did you join the service as well? :-()-: Hell no. My dad was a straight up tin soldier. Somehow he pissed this general off and got himself dishonorably discharged. Had a court martial and everything. The charges were total bullshit, so he was sure he'd get his name cleared, but it didn't happen. :-()-: Connections and politics, it's the same everywhere. :-()-: My old man, he bought into the system, and it screwed him. So he swallowed a bullet. Me, I don't believe in nothing I can't see and touch. xxx 2002
82797.0 Yorgi and Kirill. :-()-: Let me guess, they had a big white torpedo with them. xxx 2002
82786.0 What are you? :-()-: I'm an information gatherer. You wanna go somewhere and talk about it? xxx 2002
82841.0 What's this? :-()-: This is a gift. From me to you. xxx 2002
82759.0 You did say "Ahab"? You're sure about that? :-()-: Yeah, I sent you pictures. Here's what I'm thinking. If they take out the cameras and sensors, they could probably put a bomb in this thing. You drop it in the water in the Red Sea and three weeks later it's swimming up the Potomac. :-()-: This is good work, X, damn fine work. You need to press on at all costs, find out what they have planned with this "Ahab". :-()-: Hey, one miracle at a time. They're on to me now, remember? :-()-: I'm gonna be en route with a team shortly to relieve you. Just keep the pressure on until the cavalry arrives. And X? You done yourself proud helping those people today. xxx 2002
82770.0 We seen this kind of parachutes before, you know. U.S. Army. You got some friends here, jump out with you guys? :-()-: We're anti-social. We don't have any friends. xxx 2002
82819.0 French Polynesia? This wouldn't last us a week in a cheap hotel. Do you know how expensive it is down there? :-()-: Alright, so I'll get more. :-()-: It's not about the money, X! You never plan for anything. I can't live like that anymore, I need some stability. :-()-: Well if you're bailing, I guess I'd better give you your surprise now. xxx 2002
82832.0 They're all over the place! What the hell's going on? :-()-: Looks like we're in the middle of the drug war. xxx 2002
82835.0 Thanks, but I'm here on business. I heard you're the G around here. I'm looking for some cars, expensive ones. A lot of them. :-()-: Sorry, man, don't know what you're talking about. :-()-: I'm talking about the sports cars that disappear off the docks in Genoa and wind up here. If you don't know about 'em, who does? xxx 2002
82813.0 Miniature power cams set in on contact, giving you a sure grip on any surface. :-()-: They come in any other styles or colors? xxx 2002
82839.0 Very nice. I'm impressed. :-()-: I'm somewhat less so. We seem to have a bit of a problem... xxx 2002
82779.0 If you got a problem with me, why are we dancing? :-()-: Yorgi asked me to. :-()-: You do everything Yorgi says? :-()-: Go to hell. :-()-: It's gonna be like that, huh? You got all bent out of shape as soon as he started dancing with someone else. Why's that? :-()-: Mind your business. :-()-: Did you guys used to date? That's it, right? He broke your heart and you're still soft on him. That's funny, it don't seem to fit with a tough broad like you. xxx 2002
82754.0 How about a pedicure as long as you're down there. What's this? Lo-Jack? :-()-: Wherever you go on the planet, I'll find you. There's no quitting. If you try to take it off, a ring of needles will inject enough curare into your bloodstream to kill you before you hit the ground. Is all that clear? :-()-: Yeah, I spy or I die. xxx 2002
82833.0 That's the guy. :-()-: This pizda? Never seen him before. :-()-: Who you workin' for? What do you do for a living, dickhead? xxx 2002
82801.0 Toadies right behind. :-()-: Let me. xxx 2002
82816.0 From the Beastie Boys collection? :-()-: It's a stakeout suit. It's got food, water, recording gear, anything you need for covert spying. It's all- weather, fire retardant, and if you give this buckle a sharp pull, the whole outfit deploys into a parachute. :-()-: You're joking, right? xxx 2002
82826.0 I found something big enough for us to take these guys down with. They've got a of nerve agent they're gonna unleash. :-()-: Is this what you're looking for? xxx 2002
82788.0 They tell me you're an American agent. :-()-: What are you talking about? :-()-: There's no more time for games. They made you. There's a sniper out front waiting to put a bullet in your eye. Tell me if it's true. :-()-: It's a long story, but yeah, more or less. :-()-: Jesus Christ. You're going to have to go out the back. The data that you copied with your toy. Tomorrow, at six o'clock. Look it up. We're taking a trip. Be there at six and you'll have plenty to tell your people. :-()-: If I go out the back, he'll know you've warned me. :-()-: That's alright, I'll figure something out. :-()-: Not an option. Just get up slowly like everything's cool. Petra, trust me. You just hung yourself out to dry for me, I'm not gonna let you down. xxx 2002
82846.0 To us it means no walls, no speed limits, no jails. It's everybody does what he wants. People think democracy is freedom but they don't have a clue. There's an old punk song. It says: "America stands for freedom, but if you think you're free..." :-()-: "...try walking into a deli and urinating on the cheese". 'Anarchy Burger' by the Vandals. :-()-: You got it, man. It's stupid but it's true. True freedom is when you do whatever you want anytime you want. That's when you know you're living, man. :-()-: How you gonna do that with government and rules everywhere? :-()-: Easy. You get enough money that you grow an ass big enough for the whole world to kiss. xxx 2002
82793.0 What are you doing with the Ivans? :-()-: I know which cops are good and which are bad. Come with me. Come on, right now. xxx 2002
82851.0 What's the gimmick, Yorgi? That's the part I don't get. You gonna hold the world hostage with your gas bombs? I didn't think you were dumb enough to go with that hack cliche. :-()-: You think I'm after money? I told you, my friend. Anarchy. Time for a change. Money is good, but for true freedom you have to get rid of the rules themselves. :-()-: So all by yourself, you're going to destroy every government; in the world. :-()-: Easier than that, buddy. I'll get them to destroy each other. You kill an entire Peace conference, someone is going to have to pay. Then our friend Ahab continues down the river to the ocean and begins his world tour. London, Cairo, Beijing... Pretty soon everyone's involved. These guys bomb those guys, those guys invade these guys. Soon the whole world is like your wild West cowboy days. No rules, no law, everybody free to do what they want. :-()-: Including rape, pillage, murder. :-()-: Sure, if that's what you want. Why not? It's all human nature. So we just have to launch our baby and wait for the decline and fall of civilization as we know it. :-()-: And here I thought "anarchy" was just something cool to put on a Tshirt. :-()-: Come on, Xander. You used to stand for something. What happened to you, man? I thought you'd get it. xxx 2002
82844.0 Total chaos, man! Welcome to Anarchy 99! :-()-: What's "Anarchy 99"? :-()-: It's all this craziness! It's what we've been living since 99, when we left the Army. One of our brothers died in Grozny and we said the hell with this shit. What for? He dies for what? Politics? Who's politics? Not ours. :-()-: You wanna see my politics? xxx 2002
82758.0 I guess you got my E-mail. I set up a purchase. Ten cars. :-()-: If you're trying to push my buttons, you're on the right track. Don't make me question my own judgment, X. :-()-: Buddy, you sent me here to get close to their organization, that costs money. :-()-: One point two million dollars? I did not authorize you to spend one point two million dollars! :-()-: I'm already on a first name basis with these dudes, I got a deal set up, you want me to hammer it or not? :-()-: We're not after car thieves here. :-()-: What the hell am I after? You're telling me dick. :-()-: That information is classified. You're there to gather information on their operations, period. :-()-: Call me crazy, but I thought hooking up a million dollar deal was a great way to get on their good side. What else? You gonna hassle me about the weapons and spy stuff too? :-()-: My friend, if you're planning on crossing me... :-()-: I know, poison needles in my shins. You've got 36 hours. Peace, out. xxx 2002
82821.0 Sit down. :-()-: I've been on a plane for twelve hours, I think I'll stand. xxx 2002
82767.0 Ridiculous. Cut him off. I'm sorry Gibbons, you pulled up a shark this time. :-()-: I think we should send him whatever he wants. :-()-: You what? A wire transfer of this size? :-()-: If it means getting Silent Night back, absolutely. :-()-: You're talking about a very expensive risk here. :-()-: He's gotten closer in 24 hours than all of the other operatives combined. He got us an account number to boot. Now even if he's just dumb and lucky, I say we back his play. I put him out there, Sam. If he doesn't come up with the money, they'll kill him. I can't let that happen. :-()-: Why not? You were going to throw him in a tub full of acid, weren't you? xxx 2002
82749.0 Most guys we ran through this either took off or helped them rob the place. I expected you to do the same. :-()-: That shows me already that you don't have a clue. What's it to you, anyway? :-()-: Whether you thought this was for real or not, you jumped in and helped the waitress on instinct. That tells me something about your character. :-()-: Good for you. Now why don't you stop wasting my time and tell me what you want. You didn't go through all this for my benefit. :-()-: It's your lucky day. You just might get the chance to pay back our wonderful country for all the freedom you enjoy. :-()-: A Fed, I shoulda known. Who else would have this kind of budget? Now you're gonna hit me with the sales pitch. :-()-: Alright, Cage, you've got me. This is one of those moments. "Many are called, few are chosen", that kind of thing. Your government needs you. Are you up for the challenge? :-()-: ..If you're trolling around for narcs, man, have you got the wrong guy. :-()-: Why is that? :-()-: Look at me, dude, do I look like a fan of law enforcement? Forget the tests, you shoulda just asked me. I woulda saved you a lot of trouble. :-()-: Oh I don't know, I think the tests work pretty well. Sometimes they give me answers you wouldn't admit to in a million years. :-()-: Are we done now? You've got nothing on me. So if you ain't booking me, I'm walking. :-()-: You've got this wrong. You're not under arrest, you've been abducted. And until I say different, you belong to me. :-()-: Is that a fact? :-()-: Sure as gravity. I've had a feeling about you from the start, Cage. It's nausea. :-()-: You know what I hate? Any scumbag with a clean shirt and a bad haircut can get one of those tin stars and suddenly they think they're God. :-()-: You know what I hate? It's always the assholes that pass the tests. xxx 2002
82800.0 Where'd the damn truck go?! :-()-: Go to the water, it's that way! xxx 2002
82850.0 And you. Why do you have such a problem staying away, my friend? Did you miss us that much? :-()-: I wanted to get the hell out of Prague before Silent Night falls. :-()-: Not bad, man, not bad. You put things together quick. Prague it is. xxx 2002
82748.0 Who the hell are you? :-()-: The name's Gibbons. You were saying? :-()-: What is this place? :-()-: Looks like a diner. :-()-: Diner, huh? Let me tell you what the problem is. I wake up drugged to find what? You've got a "salesman" over here reading a three week old newspaper. He's pulling a 211 with a "trucker", who happens to be packing a cop issue H&K 9mm. I get tipped that something is going down when she passes me a note. How's that for twisted logic? How did she know there was trouble unless they pulled the guns before I "walked" in? And if that's true, why'd they stop and wait until I woke up to gaffle the money? Then I notice how beefy they both are. Hell, even the waitress is cut. All three of them look like they went through the same training program. Ergo they ain't strangers and this isn't what it seems. That's how I knew yo-yo wouldn't get a round off even if I gave him all day. Why? xxx 2002
82849.0 That was for Anders. :-()-: Don't forget goatee boy. I greased him up on the diamond run. xxx 2002
82794.0 Where are we going? :-()-: We're getting out of this place. We've done enough. :-()-: Wait a minute, whoa. We can't go now. What's Yorgi got planned with that nerve agent? :-()-: It doesn't matter anymore, forget it. :-()-: Of course it matters. Hey, hold on! :-()-: What do we owe our governments? Yours kidnapped you, mine abandoned me. :-()-: Petra, this ain't about the people that sent us here, you know that. :-()-: No, now it's about us. Let's do something for us before we both get killed. Don't you want to get to Bora Bora, Xander? :-()-: We can't leave now. We have to do something. :-()-: Why? You're the one who believes in nothing. Screw the world, or whatever you said. xxx 2002
82815.0 I think I'll hang on to these. :-()-: Think again, that's government property. You have to sign for everything. I've got one more that wasn't on your list. xxx 2002
82818.0 Hey, you don't need all this just to go on vacation. Unless this ain't for going on vacation... :-()-: I'm through, Xander. :-()-: I can see that. Why? :-()-: You run around with maniacs jumping motorcycles in the desert, you break 36 bones crashing wave runners and snowboarding off cliffs, all with no health insurance... It's insane, I can't take it anymore. :-()-: I'm having fun, what's the problem? :-()-: You have all this talent, and you waste it. You won't take a single endorsement deal. Meanwhile those other guys have their own video games! But no, you've got too much "integrity" for that. :-()-: I don't wanna go mersh, you know that. But hey, if this is about money... xxx 2002
82777.0 Ten is hardly worth the effort. We're talking a lousy million five U.S. :-()-: I have Japanese buyers who are looking to move a fleet, if you have the quality of merchandise they're after. And it's a mil two, max. xxx 2002
82824.0 I'll leave you two alone to talk. :-()-: Yeah, thanks a lot. xxx 2002
82752.0 Now I'm the one who's nauseous. So what's the deal? What do you need me for? :-()-: There's some folks I want to keep tabs on. Dirty, tattooed, uncivilized. Your kind of people. :-()-: What do I get out of it? :-()-: If you find out what I want to know, and if I'm able to successfully use that information, you get to go back to your degenerate little life. If not, you take a bath. :-()-: You're one sick bastard. A sadist with a badge looking to rope me in to a suicide mission. I think no matter which way I go I'm likely to wind up face down on a sheet of plastic. So here's my answer: kiss my ass, Hop-along. xxx 2002
82753.0 Not bad for a gimp. :-()-: What's with that "X" on the back of your head? Does that mean you're "extreme"? I've got some news for you, Mr. X, you're a three time loser. So maybe you should tattoo another couple of X's on your head. xxx 2002
82805.0 I can't believe my mission is finally over. I don't even know what to do with myself. :-()-: You can't welch on me now. We had a deal. xxx 2002
82762.0 Thought you bought the farm down there, X. Glad as hell to see you. :-()-: I'm pretty happy to see me too. So are you done with me yet? :-()-: You've kept up your end of the bargain, I'll do the same. But you really should consider staying on, you make a decent agent. :-()-: But I hate cops, remember? Except for her. :-()-: Cut the crap, X, I saw you down there. You're a hero. Don't be afraid to join the good guys. :-()-: Who says you're the good guys? xxx 2002
82775.0 Yorgi masterminded the take-over of three Red Mafiya clans. Cops called it "blood week". He combined all of their global enterprises into one huge crime syndicate: Anarchy 99. :-()-: "Anarchy" 99"? :-()-: What are you gonna do? They're Euro- trash. :-()-: What weapons do you have? xxx 2002
82842.0 What's going on, my friend? :-()-: You tell me! You got a sniper up there or what? :-()-: He's not with us, Xander. He must be with you. :-()-: Bullshit! You get your boy off that roof or I swear to God I'm gonna give you another hole to breathe out of. xxx 2002
82755.0 I expect you to call in regularly with progress reports. :-()-: Fine, but I'm not gonna be no Bulldog Omega 5. xxx 2002
82769.0 Whatchu doing here? :-()-: Oh, I don't know. It was an accident. :-()-: That's some accident, hombre. You accidentally fall out of a plane in the middle of the night and land up in my back yard. xxx 2002
82808.0 What are you doing? :-()-: It all happened so fast. Such an unfortunate accident. xxx 2002
82790.0 So what's the plan with this Ahab? Are they selling it or what? :-()-: No, something worse. He says it's a surprise, but I know him. This is his masterpiece. :-()-: We've got to find out what it is. Can you handle that? :-()-: There's something I didn't have time to tell you before. :-()-: What's that? :-()-: I'm a agent as well. KGB. I've been undercover for over a year. :-()-: You're what? What the hell have you been doing? :-()-: I don't know. Eight months ago I stop hearing from my people. No explanation, just silence. So I stay and I wait like I was told. Soon I'm in so far, they'd kill me if I left. Now every day I get farther and farther away from what I was. I'm like you said, a criminal. :-()-: I'm busted up for you, but Jesus, what the hell are you doing? Those people almost drowned on that boat and you didn't lift a finger. xxx 2002
82830.0 Who's writing this dude's dialogue? :-()-: He's a pretty good actor, though. xxx 2002
82802.0 You okay? :-()-: A piece of shrapnel hit me. :-()-: I'm sick of that guy. Let's stop playing nice. xxx 2002
82764.0 What do you have? :-()-: Not a whole helluva lot. His final transmission was mangled. About something or someone called "Ahab". Whatever it is, it cost him his life. :-()-: He was the best there was. That makes three agents lost. :-()-: They're ruthless and they have a lot of firepower. It's only a matter of time before they figure out how to deploy Silent Night. Then we're gonna have a catastrophe on our hands. :-()-: Silent Night in the hands of a bunch of impertinent cowboys :-()-: We're gonna have to step in, Sam. Those CIA boys couldn't find a clown in a field of cactus. Let me take care of it. :-()-: How will you fix it? :-()-: I'll go in with a team. But first I'm going to need some intel. I'll have to put someone inside, someone new, someone they won't see coming. One of their own. :-()-: So you'll dredge the bottom again. You've done that before. The results were... uneven. :-()-: You wanna find out about rats, ask a rat. I've had my people put together a talent pool. I downloaded it to your desktop. xxx 2002
82823.0 Nice hops. :-()-: The corner. Anarchy 99. xxx 2002
82780.0 Where are you going? :-()-: Why are you still hanging around? Your business is finished, you should go home. :-()-: I was invited. What's your story? :-()-: I don't know who you are or where you come from, but I don't like you. You ask too many questions. xxx 2002
82774.0 This guy's kinda dorky lookin'. :-()-: Kirill, the sniper. Looks like a bookworm, but he had 72 confirmed kills in Chechnya, they called him the "Finger of God". Assassination and weapons. xxx 2002
82829.0 Heads up, man. What's this thing on my back? :-()-: A parachute. This does not argue well. xxx 2002
82848.0 I've got meetings this afternoon, I've got to get some sleep. Just pick a girl. :-()-: That's alright, I'm kinda tired. :-()-: You want to insult me? This is my hospitality. Pick one. xxx 2002
82757.0 Is that all? :-()-: Just remember, I'll be watching. xxx 2002
82791.0 No, I guess I've been no help at all to you. :-()-: Get your head back in the game. There are lives at stake here. :-()-: Of course. I'll do what I can. I have to go, before they notice. :-()-: When can I see you again? :-()-: You can't, it's too risky. I'm not much help anyway, remember? xxx 2002
82812.0 Back off, just go away you klutz. Alright, here's the story. The items in these cases belong to me. I designed them, built them, and was going to use them in the field myself until you showed up. :-()-: I stole your beat, huh? Guess you forgot to brown-nose the right people. :-()-: Is that supposed to be funny? I'm not laughing. I've worked for ten years to get my shot at being a field agent, funny boy. And ar the last minute I get bumped by you, some reject from the Ozzfest. :-()-: Why don't you show me some gear before you get hurt. :-()-: Listen to you. Right away, sir, anything you say, sir. xxx 2002
82814.0 This is your standard dart gun. :-()-: That one I'm real familiar with. xxx 2002
47127.0 Mac's signature. :-()-: Give me a break. Remember Manzini? When he stole Montezuma's scepter he left a Pepto Bismal bottle. The best ones always copy Mac. :-()-: You're saying the thief wants us to think it's Mac but it's really not. :-()-: Exactly. entrapment 1999
47172.0 We're going to die, aren't we... :-()-: I'm not the one to ask... entrapment 1999
47178.0 Lose something? :-()-: I'm just curious what sort of security system you'd have in your own house. :-()-: And-- :-()-: I'm impressed. Can't spot a thing. :-()-: Hmmm. I'd be surprised if you could. entrapment 1999
47254.0 You can't be serious. :-()-: That's the beauty of it. There's only one tiny window of time when this will work. At the handover of Hong Kong--from Britain to China. The handover from them to us. :-()-: You're out of your mind. We can't do a job like this with no rehearsal. :-()-: There's no way to practice this. And no time. Besides, I've planned it all. There aren't any surprises. :-()-: There's always a surprise. :-()-: I've covered everything. And you're the best, so-- :-()-: You're overestimating yourself again. What's worse, this time you're overestimating me. :-()-: Look, there's a video security system to bypass, that's the only hard part. You've done that a dozen times. :-()-: For God's sake, the only way I can get from one elevator to the other is to jump. :-()-: Feeling old? entrapment 1999
47307.0 My friend, you always surprise me. :-()-: We wear so many goddam masks after a while they get stuck to our faces, you know? And they don't come off. But when you find the one person who really knows you, and then you lose her... entrapment 1999
47187.0 I go in alone. :-()-: You don't get the Mask code unless I go. entrapment 1999
47232.0 We're living history here. :-()-: You don't know the half of it. entrapment 1999
47240.0 Insidious thing, wondering if your partner...has another partner. :-()-: Okay, look, I'm delivering this to a man who's going to give us the key to our job. But it's pointless to try to explain it yet. You just have to trust me. I don't have any more secrets. :-()-: Everyone has secrets, it's what makes us human. entrapment 1999
47303.0 My antenna is up, it is fully extended, and I am picking up...what is it? It is, can it be? The boy is in el, u, v. :-()-: It's never happened before. What makes you think it's happening now? :-()-: You better be keeping your mind on business. It's not just me you got to worry about. I've got some very unpleasant folks looking over my shoulder. :-()-: You know where my loyalties lie. :-()-: To yourself, that's where. entrapment 1999
47166.0 A film case. :-()-: That's what you're buying. Put it in your pocket. entrapment 1999
47218.0 One, two, three... :-()-: You're not on the beat. entrapment 1999
47188.0 Pack up. I'll see you get back to London. :-()-: Look, I can help. You need a sensor expert. You've got one. :-()-: This isn't some Picasso print you steal out of a car dealer's rec room. entrapment 1999
47198.0 Let's do it again. :-()-: Try laps. Say a hundred. entrapment 1999
47190.0 Nice try. Everyone thinks I did. :-()-: That's because I wanted them to! :-()-: I wondered who'd been giving me a bad name. :-()-: I drilled the bolts and went in through the window. It was the only way to bypass the smart glass. :-()-: True enough. :-()-: You need a partner for this job. You'll never find one as good as me. entrapment 1999
47293.0 Right. :-()-: Because I never assume anything. :-()-: I need you to get one more thing for me. A dress, elegant but sexy, something Grace Kelly would wear. Maybe a Balenciaga. :-()-: That's it, I sure as hell ain't no personal shopper. :-()-: Black of course. entrapment 1999
47203.0 Got it. :-()-: Charged? :-()-: Charged. Receiver? entrapment 1999
47170.0 Motorcycle. :-()-: Got it. entrapment 1999
47230.0 What? :-()-: A billion dollars. entrapment 1999
47244.0 Oh my god, I thought-- :-()-: I'd taken the Mask? entrapment 1999
47259.0 No way to take another day or two? :-()-: No. You'll see when we get inside. entrapment 1999
47212.0 To us. To the Mask. :-()-: To our...partnership. entrapment 1999
47231.0 Your share. :-()-: Get the hell in. entrapment 1999
47243.0 Do you know what you've done!? Do you know? :-()-: Would you quiet down! Just for a minute? entrapment 1999
47304.0 You'll just have to have faith. :-()-: Faith is angels dancing on the head of a pin. I got to have trust. :-()-: She won't tell me everything. It's a bank job, that's all I know. entrapment 1999
47158.0 We going somewhere? :-()-: Possibly. :-()-: Maybe I should drive this time. :-()-: Maybe you should go buy yourself some clothes. entrapment 1999
47255.0 How do I look? :-()-: Like a woman of mystery. entrapment 1999
47194.0 Don't worry, I can get rid of this. No trace. And I'll even go fifty fifty, we're partners aren't we? :-()-: No. It's a down payment. entrapment 1999
47153.0 For God's sake. :-()-: I'm sorry. This just means so much to me. entrapment 1999
47221.0 Can you see the other PIRs? :-()-: Got it. entrapment 1999
47249.0 Peoples China Bank. :-()-: Where the money is. entrapment 1999
47300.0 Tonight your league night? :-()-: Check out my ball. entrapment 1999
47258.0 Done. And I assume you have the magic CD- ROM? :-()-: Surgically attached. entrapment 1999
47216.0 Camera in the-- :-()-: Bookshelf. Sensors-- :-()-: Popup, on floorboards. And-- :-()-: In eye of that painting. entrapment 1999
47292.0 Let me ask you, this Mask, when they made it--was the old bitch dead or alive? :-()-: It's a death mask. Death mask means dead. entrapment 1999
47281.0 Take the airport subway, but change at Jordan Station for Kowloon Tong. :-()-: But-- :-()-: It's only ninety seconds up the line. You're on a connection to a trans Siberian express. :-()-: What about you? entrapment 1999
47142.0 You disappeared. :-()-: You seemed to be handling everything quite nicely. :-()-: Are you...okay? :-()-: It was only a scratch. Far more damaging to my trousers than to me. :-()-: That's good. Terrific. entrapment 1999
47125.0 Meaning-- :-()-: Meaning no one arranges calalilies like that. He left the window open when he came in. His only mistake. entrapment 1999
47295.0 Oh, it is. :-()-: It better be worth it for me. entrapment 1999
47165.0 I'm telling you it's a forgery. The paint's still wet for God's sake. :-()-: Look on the back. What do you see? entrapment 1999
47136.0 How about if I try humility. :-()-: How about if you try disappearing. entrapment 1999
47152.0 Are you under the impression that now I'm in some way obligated to you? :-()-: Well, no...but... :-()-: Good. I'll call the concierge. They can get you a new room, book your flight home, so forth. entrapment 1999
47217.0 We're working. :-()-: It's a party. :-()-: Let's mingle a little, shall we? entrapment 1999
47272.0 Don't panic, now, there's no rush... :-()-: We can't leave it IN THERE, it's got all our accounts, everything that can NAIL us to a goddam CROSS!!! entrapment 1999
47195.0 On what? Another job? :-()-: We get the Mask I'll tell you. :-()-: A partner with secrets isn't much of a partner. :-()-: Without the Mask it doesn't matter. :-()-: So the Mask is part of the down payment too. Must be a really big job. :-()-: Let's just see how we do. entrapment 1999
47235.0 No, really, it's your plan, you should get at least 30 per cent-- :-()-: My eighty, your twenty, smart guy-- :-()-: It's fifty-fifty, less the cost of the dress. entrapment 1999
47223.0 Be careful not to break the laser beams. :-()-: Duh. entrapment 1999

We will use the convenient Feature extraction and transformation APIs.

Step 3. Text Tokenization

We will use the RegexTokenizer to split each document into tokens. We can setMinTokenLength() here to indicate a minimum token length, and filter away all tokens that fall below the minimum. See:

import org.apache.spark.ml.feature.RegexTokenizer

// Set params for RegexTokenizer
val tokenizer = new RegexTokenizer()
.setPattern("[\\W_]+") // break by white space character(s)
.setMinTokenLength(4) // Filter away tokens with length < 4
.setInputCol("corpus") // name of the input column
.setOutputCol("tokens") // name of the output column

// Tokenize document
val tokenized_df = tokenizer.transform(corpusDF)
import org.apache.spark.ml.feature.RegexTokenizer
tokenizer: org.apache.spark.ml.feature.RegexTokenizer = regexTok_5380a11bc0d5
tokenized_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 3 more fields]
display(tokenized_df.sample(false,0.001,1234L)) 
display(tokenized_df.sample(false,0.001,123L).select("tokens"))

Step 4. Remove Stopwords

We can easily remove stopwords using the StopWordsRemover(). See:

If a list of stopwords is not provided, the StopWordsRemover() will use this list of stopwords, also shown below, by default.

are,around,as,at,back,be,became,because,become,becomes,becoming,been,before,beforehand,behind,being,below,beside,besides,between,beyond,bill,both,bottom,but,by,call,can,cannot,cant,co,computer,con,could,
couldnt,cry,de,describe,detail,do,done,down,due,during,each,eg,eight,either,eleven,else,elsewhere,empty,enough,etc,even,ever,every,everyone,everything,everywhere,except,few,fifteen,fify,fill,find,fire,first,
five,for,former,formerly,forty,found,four,from,front,full,further,get,give,go,had,has,hasnt,have,he,hence,her,here,hereafter,hereby,herein,hereupon,hers,herself,him,himself,his,how,however,hundred,i,ie,if,
in,inc,indeed,interest,into,is,it,its,itself,keep,last,latter,latterly,least,less,ltd,made,many,may,me,meanwhile,might,mill,mine,more,moreover,most,mostly,move,much,must,my,myself,name,namely,neither,never,
nevertheless,next,nine,no,nobody,none,noone,nor,not,nothing,now,nowhere,of,off,often,on,once,one,only,onto,or,other,others,otherwise,our,ours,ourselves,out,over,own,part,per,perhaps,please,put,rather,re,same,
see,seem,seemed,seeming,seems,serious,several,she,should,show,side,since,sincere,six,sixty,so,some,somehow,someone,something,sometime,sometimes,somewhere,still,such,system,take,ten,than,that,the,their,them,
themselves,then,thence,there,thereafter,thereby,therefore,therein,thereupon,these,they,thick,thin,third,this,those,though,three,through,throughout,thru,thus,to,together,too,top,toward,towards,twelve,twenty,two,
un,under,until,up,upon,us,very,via,was,we,well,were,what,whatever,when,whence,whenever,where,whereafter,whereas,whereby,wherein,whereupon,wherever,whether,which,while,whither,who,whoever,whole,whom,whose,why,will,
with,within,without,would,yet,you,your,yours,yourself,yourselves

You can use getStopWords() to see the list of stopwords that will be used.

In this example, we will specify a list of stopwords for the StopWordsRemover() to use. We do this so that we can add on to the list later on.

display(dbutils.fs.ls("dbfs:/tmp/stopwords")) // check if the file already exists from earlier wget and dbfs-load
path name size
dbfs:/tmp/stopwords stopwords 2237.0

If the file dbfs:/tmp/stopwords already exists then skip the next two cells, otherwise download and load it into DBFS by uncommenting and evaluating the next two cells.

wget http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words -O /tmp/stopwords # uncomment '//' at the beginning and repeat only if needed again
--2019-05-31 08:23:58--  http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words
Resolving ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)... 130.209.240.253
Connecting to ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)|130.209.240.253|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2237 (2.2K)
Saving to: ‘/tmp/stopwords’

     0K ..                                                    100%  320M=0s

2019-05-31 08:23:59 (320 MB/s) - ‘/tmp/stopwords’ saved [2237/2237]
cp file:/tmp/stopwords dbfs:/tmp/stopwords 
res41: Boolean = true
// List of stopwords
val stopwords = sc.textFile("/tmp/stopwords").collect()
stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves)
stopwords.length // find the number of stopwords in the scala Array[String]
res35: Int = 319

Finally, we can just remove the stopwords using the StopWordsRemover as follows:

import org.apache.spark.ml.feature.StopWordsRemover

// Set params for StopWordsRemover
val remover = new StopWordsRemover()
.setStopWords(stopwords) // This parameter is optional
.setInputCol("tokens")
.setOutputCol("filtered")

// Create new DF with Stopwords removed
val filtered_df = remover.transform(tokenized_df)
import org.apache.spark.ml.feature.StopWordsRemover
remover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_294e3228eba8
filtered_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 4 more fields]

Step 5. Vector of Token Counts

LDA takes in a vector of token counts as input. We can use the CountVectorizer() to easily convert our text documents into vectors of token counts.

The CountVectorizer will return (VocabSize, Array(Indexed Tokens), Array(Token Frequency)).

Two handy parameters to note:

  • setMinDF: Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.
  • setMinTF: Specifies the minimum number of times a term has to appear in a document to be included in the vocabulary.

See:

import org.apache.spark.ml.feature.CountVectorizer

// Set params for CountVectorizer
val vectorizer = new CountVectorizer()
.setInputCol("filtered")
.setOutputCol("features")
.setVocabSize(10000) 
.setMinDF(5) // the minimum number of different documents a term must appear in to be included in the vocabulary.
.fit(filtered_df)
import org.apache.spark.ml.feature.CountVectorizer
vectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_48267a85f1b9
// Create vector of token counts
val countVectors = vectorizer.transform(filtered_df).select("id", "features")
countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]
// see the first countVectors
countVectors.take(1)
res38: Array[org.apache.spark.sql.Row] = Array([28762,(10000,[7,112,179,308],[1.0,1.0,1.0,1.0])])

To use the LDA algorithm in the MLlib library, we have to convert the DataFrame back into an RDD.

// Convert DF to RDD - ideally we should use ml for everything an not ml and mllib ; DAN
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector => MLVector}
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}

val lda_countVector = countVectors.map { case Row(id: Long, countVector: MLVector) => (id, Vectors.fromML(countVector)) }.rdd
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector=>MLVector}
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}
lda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[3912] at rdd at command-753740454082286:11
// format: Array(id, (VocabSize, Array(indexedTokens), Array(Token Frequency)))
lda_countVector.take(1)
res42: Array[(Long, org.apache.spark.mllib.linalg.Vector)] = Array((28762,(10000,[7,112,179,308],[1.0,1.0,1.0,1.0])))

Create LDA model with Online Variational Bayes

We will now set the parameters for LDA. We will use the OnlineLDAOptimizer() here, which implements Online Variational Bayes.

Choosing the number of topics for your LDA model requires a bit of domain knowledge. As we do not know the number of "topics", we will set numTopics to be 20.

val numTopics = 20
numTopics: Int = 20

We will set the parameters needed to build our LDA model. We can also setMiniBatchFraction for the OnlineLDAOptimizer, which sets the fraction of corpus sampled and used at each iteration. In this example, we will set this to 0.8.

import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}

// Set LDA params
val lda = new LDA()
.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))
.setK(numTopics)
.setMaxIterations(3)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@3c173c8

Create the LDA model with Online Variational Bayes.

val ldaModel = lda.run(lda_countVector)
ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@5bf00930

Watch Online Learning for Latent Dirichlet Allocation in NIPS2010 by Matt Hoffman (right click and open in new tab)

![Matt Hoffman's NIPS 2010 Talk Online LDA]](http://videolectures.net/nips2010hoffmanoll/)

Also see the paper on Online varioational Bayes by Matt linked for more details (from the above URL): http://videolectures.net/site/normaldl/tag=83534/nips20101291.pdf

Note that using the OnlineLDAOptimizer returns us a LocalLDAModel, which stores the inferred topics of your corpus.

Review Topics

We can now review the results of our LDA model. We will print out all 20 topics with their corresponding term probabilities.

Note that you will get slightly different results every time you run an LDA model since LDA includes some randomization.

Let us review results of LDA model with Online Variational Bayes, step by step.

val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4)), (Array(1, 6, 2, 0, 4),Array(0.0014443699497685366, 0.0012377629724506722, 0.0011714257476524842, 0.0010861657304183027, 8.604460434628813E-4)), (Array(1, 2, 8, 0, 3),Array(0.0014926060533533697, 0.0013429026076916017, 0.0013067364965238173, 0.0011607492289313303, 0.0011400804862230437)), (Array(5, 6, 4, 1, 7),Array(0.006717314446949222, 0.006002662754297925, 0.004488111770001314, 0.004408679383982238, 0.0042465917238892655)), (Array(0, 19, 3, 8, 6),Array(0.0050059173813691085, 0.0029731088780905225, 0.0022359962463711185, 0.002193246256785973, 0.0019111384839030116)), (Array(3, 0, 10, 1, 15),Array(0.003714410612506209, 0.0017122806517390608, 0.0017073041827440282, 0.0015712232707115927, 0.0012303967042097022)), (Array(0, 1, 6, 10, 2),Array(0.00467483294478972, 0.0038641828467113268, 0.003328578440542597, 0.002867941043688811, 0.002532629878316373)), (Array(0, 2, 9, 1, 13),Array(0.00960017865043255, 0.009308573745541343, 0.005704969701604644, 0.004085042285865179, 0.004031048471919761)), (Array(0, 4, 5, 77, 16),Array(0.004550808496981245, 0.004122146617438838, 0.0019092043643137734, 0.0018255598181846045, 0.001761167250972209)), (Array(6, 2, 5, 1, 0),Array(0.0016782125889211463, 0.0012427279906039904, 0.0012197157251243875, 0.0010635502545983016, 9.50137528050953E-4)), (Array(2, 1, 3, 0, 6),Array(0.003126597598330109, 0.0027451035751362273, 0.00228759303132256, 0.0017239166326848171, 0.0017047784964894794)), (Array(2, 1, 27, 4, 3),Array(0.004734133576359814, 0.004201386287998202, 0.0036983083453854372, 0.0025414887712607768, 0.002091795015523375)), (Array(0, 5, 1, 12, 2),Array(0.0035340054254694784, 0.002387182752907053, 0.0019263993964325303, 0.001843992584617911, 0.0018065489773133325)), (Array(2, 1, 5, 14, 0),Array(0.0016017017354850733, 0.0014834097260266685, 0.0014300356385979168, 0.001294952229819751, 0.0012788947989035501)), (Array(7, 1, 10, 6, 2),Array(0.002043769246809558, 0.0013757478946969802, 0.0013208455540129331, 0.0012662647575091633, 0.0011549537488969965)), (Array(0, 1, 2, 3, 4),Array(0.022087503347588935, 0.01571524947937798, 0.012895996754133662, 0.01026452087962411, 0.009873743305368164)), (Array(0, 1, 3, 4, 9),Array(0.002204551343207476, 0.0016283414468010306, 0.0014214537687803855, 0.0012768751041210551, 0.0011525954268574248)), (Array(46, 1, 2, 16, 5),Array(0.0022031979750387655, 0.0020637622110226085, 0.0019281346187348387, 0.0015712770524161123, 0.0014183600893726285)), (Array(0, 2, 3, 5, 8),Array(0.0035729889283848504, 0.0024215014894025766, 0.0018740761967851508, 0.001838630576321126, 0.0016262171049684524)), (Array(3, 10, 30, 9, 4),Array(0.0018098267577494882, 0.0015864305565599366, 0.0015861983258874525, 0.001331260635860306, 0.0012793651558771885)))
val vocabList = vectorizer.vocabulary
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, read, word, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, hand, drink, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, change, police, walk, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, inside, reason, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, waiting, christ, answer, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, hands, died, power, stupid, started, shouldn, months, city, boys, dinner, sense, running, hour, shoot, drive, fight, speak, george, ship, living, figure, dear, street, ahead, lady, seven, free, feeling, scared, frank, able, children, outside, moment, safe, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, lives, promise, till, sound, weren, save, poor, cool, asking, shall, plan, bitch, king, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, tired, food, company, lucky, pull, wonderful, touch, state, looked, thinks, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, explain, hotel, worse, kidding, past, outta, general, unless, felt, drop, throw, interested, hang, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, simple, turned, doin, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, smart, yesterday, fall, fault, class, bank, month, blow, major, caught, swear, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, paper, goodbye, lord, ones, strange, terrible, mistake, given, hurry, blue, finish, murder, kept, apartment, sell, middle, nothin, hasn, careful, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, future, figured, rose, agent, kinda, michael, building, mama, early, private, trip, watching, busy, record, certain, jimmy, broke, longer, sake, store, stick, finally, boat, born, sitting, evening, bucks, chief, history, ought, lying, kiss, honor, lunch, darling, favor, fool, uncle, respect, rich, liked, killing, land, peter, tough, interesting, brain, problems, nick, welcome, completely, dick, honest, wake, radio, cash, dude, dance, james, bout, floor, weird, court, calls, jail, window, involved, drunk, johnny, officer, needed, asshole, books, spend, situation, relax, pain, service, dangerous, grand, security, letter, stopped, realize, table, offer, bastard, message, instead, killer, jake, nervous, deep, pass, somethin, evil, english, bought, short, ring, step, picked, likes, voice, eddie, machine, lived, upset, forgot, carry, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, doubt, pleasure, lawyer, suit, station, gotten, bother, prove, return, pictures, slow, bunch, strong, wearing, driving, list, join, christmas, tape, attack, church, appreciate, force, hungry, standing, college, dying, present, charge, prison, missing, truck, public, board, calm, staying, gold, ball, hardly, hadn, lead, missed, island, government, horse, cover, reach, french, joke, star, fish, mike, moved, america, surprise, soul, seconds, club, self, movies, putting, dress, cost, listening, lots, price, saved, smell, mark, peace, gives, crime, dreams, entire, single, usually, department, beer, holy, west, wall, stuck, nose, protect, ways, teach, awful, forever, type, grow, train, detective, billy, rock, planet, walking, beginning, dumb, papers, folks, park, attention, hide, card, birthday, reading, test, share, master, lieutenant, starting, field, partner, twice, enjoy, dollar, blame, film, mess, bomb, round, girlfriend, south, loves, plenty, using, gentlemen, especially, records, evidence, experience, silly, admit, normal, fired, talkin, lock, louis, fighting, mission, notice, memory, promised, crap, wedding, orders, ground, guns, glass, marriage, idiot, heaven, impossible, knock, green, wondering, spent, animal, hole, neck, drugs, press, nuts, names, broken, position, asleep, jerry, visit, boyfriend, acting, plans, feels, tells, paris, smoke, wind, sheriff, cross, holding, gimme, mention, walked, judge, code, double, brothers, writing, pardon, keeps, fellow, fell, closed, angry, lovely, cute, surprised, percent, charles, correct, agree, bathroom, address, andy, ridiculous, summer, tommy, rules, note, account, group, sleeping, learned, sing, pulled, colonel, proud, laugh, river, area, upstairs, jump, built, difficult, breakfast, bobby, bridge, dirty, betty, amazing, locked, north, definitely, alex, feelings, plus, worst, accept, kick, file, wild, seriously, grace, stories, steal, gettin, nature, advice, relationship, contact, waste, places, spot, beach, stole, apart, favorite, knowing, level, song, faith, risk, loose, patient, foot, eating, played, action, witness, washington, turns, build, obviously, begin, split, games, command, crew, decide, nurse, keeping, tight, bird, form, runs, copy, arrest, complete, scene, consider, jeffrey, insane, taste, teeth, shoes, monster, devil, henry, career, sooner, innocent, hall, showed, gift, weekend, heavy, study, greatest, comin, danger, keys, raise, destroy, track, carl, california, concerned, bruce, program, blind, suddenly, hanging, apologize, seventy, chicken, medical, forward, drinking, sweetheart, willing, guard, legs, admiral, shop, professor, suspect, tree, camp, data, ticket, goodnight, possibly, dunno, burn, paying, television, trick, murdered, losing, senator, credit, extra, dropped, sold, warm, meaning, stone, starts, hiding, lately, cheap, marty, taught, science, lookin, simply, majesty, harold, corner, jeff, queen, following, duty, training, seat, heads, cars, discuss, bear, noticed, enemy, helped, screw, richard, flight)
val topics = topicIndices.map { case (terms, termWeights) =>
  terms.map(vocabList(_)).zip(termWeights)
}
topics: Array[Array[(String, Double)]] = Array(Array((just,0.0014102155338741765), (like,0.0012758924372910556), (think,0.0011214448310395873), (home,9.238914780871355E-4), (know,9.047647243869576E-4)), Array((just,0.0014443699497685366), (going,0.0012377629724506722), (like,0.0011714257476524842), (know,0.0010861657304183027), (think,8.604460434628813E-4)), Array((just,0.0014926060533533697), (like,0.0013429026076916017), (yeah,0.0013067364965238173), (know,0.0011607492289313303), (want,0.0011400804862230437)), Array((right,0.006717314446949222), (going,0.006002662754297925), (think,0.004488111770001314), (just,0.004408679383982238), (good,0.0042465917238892655)), Array((know,0.0050059173813691085), (sure,0.0029731088780905225), (want,0.0022359962463711185), (yeah,0.002193246256785973), (going,0.0019111384839030116)), Array((want,0.003714410612506209), (know,0.0017122806517390608), (come,0.0017073041827440282), (just,0.0015712232707115927), (make,0.0012303967042097022)), Array((know,0.00467483294478972), (just,0.0038641828467113268), (going,0.003328578440542597), (come,0.002867941043688811), (like,0.002532629878316373)), Array((know,0.00960017865043255), (like,0.009308573745541343), (tell,0.005704969701604644), (just,0.004085042285865179), (didn,0.004031048471919761)), Array((know,0.004550808496981245), (think,0.004122146617438838), (right,0.0019092043643137734), (fucking,0.0018255598181846045), (okay,0.001761167250972209)), Array((going,0.0016782125889211463), (like,0.0012427279906039904), (right,0.0012197157251243875), (just,0.0010635502545983016), (know,9.50137528050953E-4)), Array((like,0.003126597598330109), (just,0.0027451035751362273), (want,0.00228759303132256), (know,0.0017239166326848171), (going,0.0017047784964894794)), Array((like,0.004734133576359814), (just,0.004201386287998202), (love,0.0036983083453854372), (think,0.0025414887712607768), (want,0.002091795015523375)), Array((know,0.0035340054254694784), (right,0.002387182752907053), (just,0.0019263993964325303), (look,0.001843992584617911), (like,0.0018065489773133325)), Array((like,0.0016017017354850733), (just,0.0014834097260266685), (right,0.0014300356385979168), (mean,0.001294952229819751), (know,0.0012788947989035501)), Array((good,0.002043769246809558), (just,0.0013757478946969802), (come,0.0013208455540129331), (going,0.0012662647575091633), (like,0.0011549537488969965)), Array((know,0.022087503347588935), (just,0.01571524947937798), (like,0.012895996754133662), (want,0.01026452087962411), (think,0.009873743305368164)), Array((know,0.002204551343207476), (just,0.0016283414468010306), (want,0.0014214537687803855), (think,0.0012768751041210551), (tell,0.0011525954268574248)), Array((hell,0.0022031979750387655), (just,0.0020637622110226085), (like,0.0019281346187348387), (okay,0.0015712770524161123), (right,0.0014183600893726285)), Array((know,0.0035729889283848504), (like,0.0024215014894025766), (want,0.0018740761967851508), (right,0.001838630576321126), (yeah,0.0016262171049684524)), Array((want,0.0018098267577494882), (come,0.0015864305565599366), (doing,0.0015861983258874525), (tell,0.001331260635860306), (think,0.0012793651558771885)))

Feel free to take things apart to understand!

topicIndices(0)
res43: (Array[Int], Array[Double]) = (Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4))
topicIndices(0)._1
res44: Array[Int] = Array(1, 2, 4, 49, 0)
topicIndices(0)._1(0)
res45: Int = 1
vocabList(topicIndices(0)._1(0))
res46: String = just

Review Results of LDA model with Online Variational Bayes - Doing all four steps earlier at once.

val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
  terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
  println(s"TOPIC $i")
  topic.foreach { case (term, weight) => println(s"$term\t$weight") }
  println(s"==========")
}
20 topics:
TOPIC 0
just	0.0014102155338741765
like	0.0012758924372910556
think	0.0011214448310395873
home	9.238914780871355E-4
know	9.047647243869576E-4
==========
TOPIC 1
just	0.0014443699497685366
going	0.0012377629724506722
like	0.0011714257476524842
know	0.0010861657304183027
think	8.604460434628813E-4
==========
TOPIC 2
just	0.0014926060533533697
like	0.0013429026076916017
yeah	0.0013067364965238173
know	0.0011607492289313303
want	0.0011400804862230437
==========
TOPIC 3
right	0.006717314446949222
going	0.006002662754297925
think	0.004488111770001314
just	0.004408679383982238
good	0.0042465917238892655
==========
TOPIC 4
know	0.0050059173813691085
sure	0.0029731088780905225
want	0.0022359962463711185
yeah	0.002193246256785973
going	0.0019111384839030116
==========
TOPIC 5
want	0.003714410612506209
know	0.0017122806517390608
come	0.0017073041827440282
just	0.0015712232707115927
make	0.0012303967042097022
==========
TOPIC 6
know	0.00467483294478972
just	0.0038641828467113268
going	0.003328578440542597
come	0.002867941043688811
like	0.002532629878316373
==========
TOPIC 7
know	0.00960017865043255
like	0.009308573745541343
tell	0.005704969701604644
just	0.004085042285865179
didn	0.004031048471919761
==========
TOPIC 8
know	0.004550808496981245
think	0.004122146617438838
right	0.0019092043643137734
fucking	0.0018255598181846045
okay	0.001761167250972209
==========
TOPIC 9
going	0.0016782125889211463
like	0.0012427279906039904
right	0.0012197157251243875
just	0.0010635502545983016
know	9.50137528050953E-4
==========
TOPIC 10
like	0.003126597598330109
just	0.0027451035751362273
want	0.00228759303132256
know	0.0017239166326848171
going	0.0017047784964894794
==========
TOPIC 11
like	0.004734133576359814
just	0.004201386287998202
love	0.0036983083453854372
think	0.0025414887712607768
want	0.002091795015523375
==========
TOPIC 12
know	0.0035340054254694784
right	0.002387182752907053
just	0.0019263993964325303
look	0.001843992584617911
like	0.0018065489773133325
==========
TOPIC 13
like	0.0016017017354850733
just	0.0014834097260266685
right	0.0014300356385979168
mean	0.001294952229819751
know	0.0012788947989035501
==========
TOPIC 14
good	0.002043769246809558
just	0.0013757478946969802
come	0.0013208455540129331
going	0.0012662647575091633
like	0.0011549537488969965
==========
TOPIC 15
know	0.022087503347588935
just	0.01571524947937798
like	0.012895996754133662
want	0.01026452087962411
think	0.009873743305368164
==========
TOPIC 16
know	0.002204551343207476
just	0.0016283414468010306
want	0.0014214537687803855
think	0.0012768751041210551
tell	0.0011525954268574248
==========
TOPIC 17
hell	0.0022031979750387655
just	0.0020637622110226085
like	0.0019281346187348387
okay	0.0015712770524161123
right	0.0014183600893726285
==========
TOPIC 18
know	0.0035729889283848504
like	0.0024215014894025766
want	0.0018740761967851508
right	0.001838630576321126
yeah	0.0016262171049684524
==========
TOPIC 19
want	0.0018098267577494882
come	0.0015864305565599366
doing	0.0015861983258874525
tell	0.001331260635860306
think	0.0012793651558771885
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4)), (Array(1, 6, 2, 0, 4),Array(0.0014443699497685366, 0.0012377629724506722, 0.0011714257476524842, 0.0010861657304183027, 8.604460434628813E-4)), (Array(1, 2, 8, 0, 3),Array(0.0014926060533533697, 0.0013429026076916017, 0.0013067364965238173, 0.0011607492289313303, 0.0011400804862230437)), (Array(5, 6, 4, 1, 7),Array(0.006717314446949222, 0.006002662754297925, 0.004488111770001314, 0.004408679383982238, 0.0042465917238892655)), (Array(0, 19, 3, 8, 6),Array(0.0050059173813691085, 0.0029731088780905225, 0.0022359962463711185, 0.002193246256785973, 0.0019111384839030116)), (Array(3, 0, 10, 1, 15),Array(0.003714410612506209, 0.0017122806517390608, 0.0017073041827440282, 0.0015712232707115927, 0.0012303967042097022)), (Array(0, 1, 6, 10, 2),Array(0.00467483294478972, 0.0038641828467113268, 0.003328578440542597, 0.002867941043688811, 0.002532629878316373)), (Array(0, 2, 9, 1, 13),Array(0.00960017865043255, 0.009308573745541343, 0.005704969701604644, 0.004085042285865179, 0.004031048471919761)), (Array(0, 4, 5, 77, 16),Array(0.004550808496981245, 0.004122146617438838, 0.0019092043643137734, 0.0018255598181846045, 0.001761167250972209)), (Array(6, 2, 5, 1, 0),Array(0.0016782125889211463, 0.0012427279906039904, 0.0012197157251243875, 0.0010635502545983016, 9.50137528050953E-4)), (Array(2, 1, 3, 0, 6),Array(0.003126597598330109, 0.0027451035751362273, 0.00228759303132256, 0.0017239166326848171, 0.0017047784964894794)), (Array(2, 1, 27, 4, 3),Array(0.004734133576359814, 0.004201386287998202, 0.0036983083453854372, 0.0025414887712607768, 0.002091795015523375)), (Array(0, 5, 1, 12, 2),Array(0.0035340054254694784, 0.002387182752907053, 0.0019263993964325303, 0.001843992584617911, 0.0018065489773133325)), (Array(2, 1, 5, 14, 0),Array(0.0016017017354850733, 0.0014834097260266685, 0.0014300356385979168, 0.001294952229819751, 0.0012788947989035501)), (Array(7, 1, 10, 6, 2),Array(0.002043769246809558, 0.0013757478946969802, 0.0013208455540129331, 0.0012662647575091633, 0.0011549537488969965)), (Array(0, 1, 2, 3, 4),Array(0.022087503347588935, 0.01571524947937798, 0.012895996754133662, 0.01026452087962411, 0.009873743305368164)), (Array(0, 1, 3, 4, 9),Array(0.002204551343207476, 0.0016283414468010306, 0.0014214537687803855, 0.0012768751041210551, 0.0011525954268574248)), (Array(46, 1, 2, 16, 5),Array(0.0022031979750387655, 0.0020637622110226085, 0.0019281346187348387, 0.0015712770524161123, 0.0014183600893726285)), (Array(0, 2, 3, 5, 8),Array(0.0035729889283848504, 0.0024215014894025766, 0.0018740761967851508, 0.001838630576321126, 0.0016262171049684524)), (Array(3, 10, 30, 9, 4),Array(0.0018098267577494882, 0.0015864305565599366, 0.0015861983258874525, 0.001331260635860306, 0.0012793651558771885)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, read, word, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, hand, drink, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, change, police, walk, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, inside, reason, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, waiting, christ, answer, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, hands, died, power, stupid, started, shouldn, months, city, boys, dinner, sense, running, hour, shoot, drive, fight, speak, george, ship, living, figure, dear, street, ahead, lady, seven, free, feeling, scared, frank, able, children, outside, moment, safe, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, lives, promise, till, sound, weren, save, poor, cool, asking, shall, plan, bitch, king, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, tired, food, company, lucky, pull, wonderful, touch, state, looked, thinks, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, explain, hotel, worse, kidding, past, outta, general, unless, felt, drop, throw, interested, hang, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, simple, turned, doin, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, smart, yesterday, fall, fault, class, bank, month, blow, major, caught, swear, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, paper, goodbye, lord, ones, strange, terrible, mistake, given, hurry, blue, finish, murder, kept, apartment, sell, middle, nothin, hasn, careful, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, future, figured, rose, agent, kinda, michael, building, mama, early, private, trip, watching, busy, record, certain, jimmy, broke, longer, sake, store, stick, finally, boat, born, sitting, evening, bucks, chief, history, ought, lying, kiss, honor, lunch, darling, favor, fool, uncle, respect, rich, liked, killing, land, peter, tough, interesting, brain, problems, nick, welcome, completely, dick, honest, wake, radio, cash, dude, dance, james, bout, floor, weird, court, calls, jail, window, involved, drunk, johnny, officer, needed, asshole, books, spend, situation, relax, pain, service, dangerous, grand, security, letter, stopped, realize, table, offer, bastard, message, instead, killer, jake, nervous, deep, pass, somethin, evil, english, bought, short, ring, step, picked, likes, voice, eddie, machine, lived, upset, forgot, carry, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, doubt, pleasure, lawyer, suit, station, gotten, bother, prove, return, pictures, slow, bunch, strong, wearing, driving, list, join, christmas, tape, attack, church, appreciate, force, hungry, standing, college, dying, present, charge, prison, missing, truck, public, board, calm, staying, gold, ball, hardly, hadn, lead, missed, island, government, horse, cover, reach, french, joke, star, fish, mike, moved, america, surprise, soul, seconds, club, self, movies, putting, dress, cost, listening, lots, price, saved, smell, mark, peace, gives, crime, dreams, entire, single, usually, department, beer, holy, west, wall, stuck, nose, protect, ways, teach, awful, forever, type, grow, train, detective, billy, rock, planet, walking, beginning, dumb, papers, folks, park, attention, hide, card, birthday, reading, test, share, master, lieutenant, starting, field, partner, twice, enjoy, dollar, blame, film, mess, bomb, round, girlfriend, south, loves, plenty, using, gentlemen, especially, records, evidence, experience, silly, admit, normal, fired, talkin, lock, louis, fighting, mission, notice, memory, promised, crap, wedding, orders, ground, guns, glass, marriage, idiot, heaven, impossible, knock, green, wondering, spent, animal, hole, neck, drugs, press, nuts, names, broken, position, asleep, jerry, visit, boyfriend, acting, plans, feels, tells, paris, smoke, wind, sheriff, cross, holding, gimme, mention, walked, judge, code, double, brothers, writing, pardon, keeps, fellow, fell, closed, angry, lovely, cute, surprised, percent, charles, correct, agree, bathroom, address, andy, ridiculous, summer, tommy, rules, note, account, group, sleeping, learned, sing, pulled, colonel, proud, laugh, river, area, upstairs, jump, built, difficult, breakfast, bobby, bridge, dirty, betty, amazing, locked, north, definitely, alex, feelings, plus, worst, accept, kick, file, wild, seriously, grace, stories, steal, gettin, nature, advice, relationship, contact, waste, places, spot, beach, stole, apart, favorite, knowing, level, song, faith, risk, loose, patient, foot, eating, played, action, witness, washington, turns, build, obviously, begin, split, games, command, crew, decide, nurse, keeping, tight, bird, form, runs, copy, arrest, complete, scene, consider, jeffrey, insane, taste, teeth, shoes, monster, devil, henry, career, sooner, innocent, hall, showed, gift, weekend, heavy, study, greatest, comin, danger, keys, raise, destroy, track, carl, california, concerned, bruce, program, blind, suddenly, hanging, apologize, seventy, chicken, medical, forward, drinking, sweetheart, willing, guard, legs, admiral, shop, professor, suspect, tree, camp, data, ticket, goodnight, possibly, dunno, burn, paying, television, trick, murdered, losing, senator, credit, extra, dropped, sold, warm, meaning, stone, starts, hiding, lately, cheap, marty, taught, science, lookin, simply, majesty, harold, corner, jeff, queen, following, duty, training, seat, heads, cars, discuss, bear, noticed, enemy, helped, screw, richard, flight)
topics: Array[Array[(String, Double)]] = Array(Array((just,0.0014102155338741765), (like,0.0012758924372910556), (think,0.0011214448310395873), (home,9.238914780871355E-4), (know,9.047647243869576E-4)), Array((just,0.0014443699497685366), (going,0.0012377629724506722), (like,0.0011714257476524842), (know,0.0010861657304183027), (think,8.604460434628813E-4)), Array((just,0.0014926060533533697), (like,0.0013429026076916017), (yeah,0.0013067364965238173), (know,0.0011607492289313303), (want,0.0011400804862230437)), Array((right,0.006717314446949222), (going,0.006002662754297925), (think,0.004488111770001314), (just,0.004408679383982238), (good,0.0042465917238892655)), Array((know,0.0050059173813691085), (sure,0.0029731088780905225), (want,0.0022359962463711185), (yeah,0.002193246256785973), (going,0.0019111384839030116)), Array((want,0.003714410612506209), (know,0.0017122806517390608), (come,0.0017073041827440282), (just,0.0015712232707115927), (make,0.0012303967042097022)), Array((know,0.00467483294478972), (just,0.0038641828467113268), (going,0.003328578440542597), (come,0.002867941043688811), (like,0.002532629878316373)), Array((know,0.00960017865043255), (like,0.009308573745541343), (tell,0.005704969701604644), (just,0.004085042285865179), (didn,0.004031048471919761)), Array((know,0.004550808496981245), (think,0.004122146617438838), (right,0.0019092043643137734), (fucking,0.0018255598181846045), (okay,0.001761167250972209)), Array((going,0.0016782125889211463), (like,0.0012427279906039904), (right,0.0012197157251243875), (just,0.0010635502545983016), (know,9.50137528050953E-4)), Array((like,0.003126597598330109), (just,0.0027451035751362273), (want,0.00228759303132256), (know,0.0017239166326848171), (going,0.0017047784964894794)), Array((like,0.004734133576359814), (just,0.004201386287998202), (love,0.0036983083453854372), (think,0.0025414887712607768), (want,0.002091795015523375)), Array((know,0.0035340054254694784), (right,0.002387182752907053), (just,0.0019263993964325303), (look,0.001843992584617911), (like,0.0018065489773133325)), Array((like,0.0016017017354850733), (just,0.0014834097260266685), (right,0.0014300356385979168), (mean,0.001294952229819751), (know,0.0012788947989035501)), Array((good,0.002043769246809558), (just,0.0013757478946969802), (come,0.0013208455540129331), (going,0.0012662647575091633), (like,0.0011549537488969965)), Array((know,0.022087503347588935), (just,0.01571524947937798), (like,0.012895996754133662), (want,0.01026452087962411), (think,0.009873743305368164)), Array((know,0.002204551343207476), (just,0.0016283414468010306), (want,0.0014214537687803855), (think,0.0012768751041210551), (tell,0.0011525954268574248)), Array((hell,0.0022031979750387655), (just,0.0020637622110226085), (like,0.0019281346187348387), (okay,0.0015712770524161123), (right,0.0014183600893726285)), Array((know,0.0035729889283848504), (like,0.0024215014894025766), (want,0.0018740761967851508), (right,0.001838630576321126), (yeah,0.0016262171049684524)), Array((want,0.0018098267577494882), (come,0.0015864305565599366), (doing,0.0015861983258874525), (tell,0.001331260635860306), (think,0.0012793651558771885)))

Going through the results, you may notice that some of the topic words returned are actually stopwords that are specific to our dataset (for eg: "writes", "article"...). Let's try improving our model.

Step 8. Model Tuning - Refilter Stopwords

We will try to improve the results of our model by identifying some stopwords that are specific to our dataset. We will filter these stopwords out and rerun our LDA model to see if we get better results.

val add_stopwords = Array("whatever") // add  more stop-words like the name of your company!
add_stopwords: Array[String] = Array(whatever)
// Combine newly identified stopwords to our exising list of stopwords
val new_stopwords = stopwords.union(add_stopwords)
new_stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves, whatever)
import org.apache.spark.ml.feature.StopWordsRemover

// Set Params for StopWordsRemover with new_stopwords
val remover = new StopWordsRemover()
.setStopWords(new_stopwords)
.setInputCol("tokens")
.setOutputCol("filtered")

// Create new df with new list of stopwords removed
val new_filtered_df = remover.transform(tokenized_df)
import org.apache.spark.ml.feature.StopWordsRemover
remover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_3d7dc1a9b2ef
new_filtered_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 4 more fields]
// Set Params for CountVectorizer
val vectorizer = new CountVectorizer()
.setInputCol("filtered")
.setOutputCol("features")
.setVocabSize(10000)
.setMinDF(5)
.fit(new_filtered_df)

// Create new df of countVectors
val new_countVectors = vectorizer.transform(new_filtered_df).select("id", "features")
vectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_2fcb7a8b0dc8
new_countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]
// Convert DF to RDD
val new_lda_countVector = new_countVectors.map { case Row(id: Long, countVector: MLVector) => (id, Vectors.fromML(countVector)) }.rdd
new_lda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[3955] at rdd at command-753740454082314:2

We will also increase MaxIterations to 10 to see if we get better results.

// Set LDA parameters
val new_lda = new LDA()
.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))
.setK(numTopics)
.setMaxIterations(10)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
new_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@5fca2e4f

How to find what the default values are?

Dive into the source!!!

  1. Let's find the default value for docConcentration now.
  2. Got to Apache Spark package Root: https://spark.apache.org/docs/latest/api/scala/#package
  • search for 'ml' in the search box on the top left (ml is for ml library)

  • Then find the LDA by scrolling below on the left to mllib's clustering methods and click on LDA

  • Then click on the source code link which should take you here:

    /**
     * Concentration parameter (commonly named "alpha") for the prior placed on documents'
     * distributions over topics ("theta").
     *
     * This is the parameter to a Dirichlet distribution, where larger values mean more smoothing
     * (more regularization).
     *
     * If not set by the user, then docConcentration is set automatically. If set to
     * singleton vector [alpha], then alpha is replicated to a vector of length k in fitting.
     * Otherwise, the [[docConcentration]] vector must be length k.
     * (default = automatic)
     *
     * Optimizer-specific parameter settings:
     *  - EM
     *     - Currently only supports symmetric distributions, so all values in the vector should be
     *       the same.
     *     - Values should be > 1.0
     *     - default = uniformly (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows
     *       from Asuncion et al. (2009), who recommend a +1 adjustment for EM.
     *  - Online
     *     - Values should be >= 0
     *     - default = uniformly (1.0 / k), following the implementation from
     *       [[https://github.com/Blei-Lab/onlineldavb]].
     * @group param
     */
    

HOMEWORK: Try to find the default value for TopicConcentration.

// Create LDA model with stopwords refiltered
val new_ldaModel = new_lda.run(new_lda_countVector)
new_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@3f1301a7
val topicIndices = new_ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
  terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
  println(s"TOPIC $i")
  topic.foreach { case (term, weight) => println(s"$term\t$weight") }
  println(s"==========")
}
20 topics:
TOPIC 0
right	0.002368539995607174
love	0.0019026093436816463
just	0.001739005396343051
okay	0.001493567868602809
know	0.0011919944841106388
==========
TOPIC 1
like	0.012255569993473736
just	0.007532527227834193
come	0.007114873840600518
know	0.006960825682483897
think	0.006460380113586568
==========
TOPIC 2
know	0.017593342399778864
yeah	0.01729763439457538
gonna	0.014297985209693677
just	0.009395640487800467
tell	0.007112117826339655
==========
TOPIC 3
just	0.002310885836348927
know	0.0020049203493508585
better	0.001839601963450054
like	0.0016545385663972387
right	0.001505081787498549
==========
TOPIC 4
know	0.012396058201765845
didn	0.004786910731106122
like	0.004783067030382327
right	0.003733205551673614
just	0.0028592628116592403
==========
TOPIC 5
just	0.0028236500929191208
know	0.0026011344347436015
going	0.0015951009390631876
didn	0.001385667983895007
wait	0.001275555813151892
==========
TOPIC 6
going	0.00275337137203844
right	0.001685679960504387
just	0.0015380845174617235
know	0.0014818062892167352
captain	0.0013896743515293423
==========
TOPIC 7
going	0.011956735401221285
just	0.006541063462593452
know	0.005428932374204778
think	0.004308569608730405
believe	0.003696595226603709
==========
TOPIC 8
think	0.0019959039820595533
sorry	0.00198077299794292
know	0.0016723315231586236
shit	0.0015606901977245095
right	0.0013015271817698212
==========
TOPIC 9
know	0.003615862714921936
said	0.001961114693915351
sorry	0.0018595382287745752
like	0.0017819242854891695
think	0.0016468683030306027
==========
TOPIC 10
time	0.008784671423019166
want	0.00282365356227211
sure	0.0024833597381016476
know	0.0019777615447230884
right	0.0016576304456760946
==========
TOPIC 11
just	0.0021068918389201634
like	0.0020497480766035994
know	0.002022347553873645
want	0.0019500819941038825
said	0.001503771370040063
==========
TOPIC 12
look	0.00433587608823225
think	0.0025833796049907604
know	0.002007970741805987
going	0.0016840410422251017
just	0.0010661551551733228
==========
TOPIC 13
know	0.0020279945673448915
come	0.0019980250335794405
think	0.0012733121858788797
going	0.001192108885417234
okay	0.001186180285931844
==========
TOPIC 14
like	0.004262090436242644
right	0.0021537790725358777
just	0.0013683197398457016
know	0.0010911699327713488
look	0.0010869000557749361
==========
TOPIC 15
come	0.004769396664496132
know	0.0026229974920448534
like	0.0021612642420959253
just	0.0013228057897488347
right	0.001171812635848879
==========
TOPIC 16
know	0.025323543461007635
just	0.018361261941348715
like	0.01574431601713426
want	0.014855701536091734
think	0.011957607420818889
==========
TOPIC 17
like	0.004346004035796333
know	0.0022903208899377127
just	0.002008680613491114
little	0.0019547134832950414
maybe	0.0017287784612649724
==========
TOPIC 18
know	0.003217184151682409
think	0.003063734585623867
just	0.0018328245079520728
want	0.0017709019452594528
like	0.0016903614729120188
==========
TOPIC 19
hello	0.008911727886543675
stop	0.0025143616929346174
just	0.0023958078165974795
like	0.00184251815055585
come	0.0018199130672157007
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(5, 27, 1, 16, 0),Array(0.002368539995607174, 0.0019026093436816463, 0.001739005396343051, 0.001493567868602809, 0.0011919944841106388)), (Array(2, 1, 10, 0, 4),Array(0.012255569993473736, 0.007532527227834193, 0.007114873840600518, 0.006960825682483897, 0.006460380113586568)), (Array(0, 8, 20, 1, 9),Array(0.017593342399778864, 0.01729763439457538, 0.014297985209693677, 0.009395640487800467, 0.007112117826339655)), (Array(1, 0, 36, 2, 5),Array(0.002310885836348927, 0.0020049203493508585, 0.001839601963450054, 0.0016545385663972387, 0.001505081787498549)), (Array(0, 13, 2, 5, 1),Array(0.012396058201765845, 0.004786910731106122, 0.004783067030382327, 0.003733205551673614, 0.0028592628116592403)), (Array(1, 0, 6, 13, 57),Array(0.0028236500929191208, 0.0026011344347436015, 0.0015951009390631876, 0.001385667983895007, 0.001275555813151892)), (Array(6, 5, 1, 0, 233),Array(0.00275337137203844, 0.001685679960504387, 0.0015380845174617235, 0.0014818062892167352, 0.0013896743515293423)), (Array(6, 1, 0, 4, 40),Array(0.011956735401221285, 0.006541063462593452, 0.005428932374204778, 0.004308569608730405, 0.003696595226603709)), (Array(4, 26, 0, 42, 5),Array(0.0019959039820595533, 0.00198077299794292, 0.0016723315231586236, 0.0015606901977245095, 0.0013015271817698212)), (Array(0, 23, 26, 2, 4),Array(0.003615862714921936, 0.001961114693915351, 0.0018595382287745752, 0.0017819242854891695, 0.0016468683030306027)), (Array(11, 3, 19, 0, 5),Array(0.008784671423019166, 0.00282365356227211, 0.0024833597381016476, 0.0019777615447230884, 0.0016576304456760946)), (Array(1, 2, 0, 3, 23),Array(0.0021068918389201634, 0.0020497480766035994, 0.002022347553873645, 0.0019500819941038825, 0.001503771370040063)), (Array(12, 4, 0, 6, 1),Array(0.00433587608823225, 0.0025833796049907604, 0.002007970741805987, 0.0016840410422251017, 0.0010661551551733228)), (Array(0, 10, 4, 6, 16),Array(0.0020279945673448915, 0.0019980250335794405, 0.0012733121858788797, 0.001192108885417234, 0.001186180285931844)), (Array(2, 5, 1, 0, 12),Array(0.004262090436242644, 0.0021537790725358777, 0.0013683197398457016, 0.0010911699327713488, 0.0010869000557749361)), (Array(10, 0, 2, 1, 5),Array(0.004769396664496132, 0.0026229974920448534, 0.0021612642420959253, 0.0013228057897488347, 0.001171812635848879)), (Array(0, 1, 2, 3, 4),Array(0.025323543461007635, 0.018361261941348715, 0.01574431601713426, 0.014855701536091734, 0.011957607420818889)), (Array(2, 0, 1, 18, 24),Array(0.004346004035796333, 0.0022903208899377127, 0.002008680613491114, 0.0019547134832950414, 0.0017287784612649724)), (Array(0, 4, 1, 3, 2),Array(0.003217184151682409, 0.003063734585623867, 0.0018328245079520728, 0.0017709019452594528, 0.0016903614729120188)), (Array(121, 68, 1, 2, 10),Array(0.008911727886543675, 0.0025143616929346174, 0.0023958078165974795, 0.00184251815055585, 0.0018199130672157007)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, school, today, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, happy, open, lost, john, hurt, town, ready, alright, late, actually, gave, married, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, number, tried, means, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, waiting, christ, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, million, fact, hands, died, power, started, stupid, shouldn, months, boys, city, sense, dinner, running, hour, shoot, drive, fight, speak, george, living, ship, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, outside, safe, moment, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sister, sounds, lives, till, promise, sound, weren, save, poor, cool, asking, shall, plan, king, bitch, daughter, beat, weeks, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, food, tired, company, lucky, pull, wonderful, touch, looked, state, thinks, picture, words, leaving, control, clear, known, special, buddy, luck, follow, order, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, coffee, takes, ride, parents, miles, works, secret, hotel, explain, worse, kidding, past, outta, general, unless, felt, drop, throw, hang, interested, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, doin, turned, simple, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, yesterday, smart, fall, fault, class, bank, month, blow, swear, caught, major, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, ones, lord, goodbye, paper, terrible, strange, mistake, given, kept, finish, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, fair, imagine, difference, quiet, happening, near, quit, personal, marry, figured, rose, future, building, kinda, agent, early, mama, michael, watching, trip, private, busy, record, certain, jimmy, broke, longer, sake, store, finally, boat, stick, born, sitting, evening, bucks, history, chief, lying, ought, honor, kiss, darling, lunch, uncle, fool, favor, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, welcome, nick, problems, wake, radio, dick, honest, cash, dance, dude, james, bout, floor, weird, court, jail, calls, window, involved, drunk, johnny, officer, needed, asshole, spend, situation, books, relax, pain, grand, dangerous, service, letter, stopped, security, realize, offer, table, message, bastard, killer, instead, jake, deep, nervous, pass, somethin, evil, english, bought, short, step, ring, picked, likes, machine, voice, eddie, upset, carry, forgot, lived, afternoon, fear, finished, quick, count, forgive, wrote, named, decided, totally, space, team, lawyer, pleasure, doubt, suit, station, gotten, bother, return, prove, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, force, church, attack, appreciate, college, standing, hungry, present, dying, charge, prison, missing, truck, board, public, staying, calm, gold, ball, hardly, hadn, lead, missed, island, government, cover, horse, reach, joke, french, fish, star, america, moved, soul, surprise, mike, putting, seconds, club, self, movies, dress, cost, lots, price, listening, saved, smell, mark, peace, dreams, crime, gives, entire, department, usually, single, holy, west, beer, nose, wall, stuck, protect, ways, teach, train, grow, awful, type, forever, rock, detective, billy, dumb, papers, walking, beginning, planet, folks, park, attention, card, hide, birthday, master, share, lieutenant, starting, test, reading, field, partner, twice, enjoy, film, bomb, mess, blame, dollar, loves, girlfriend, south, round, records, especially, using, plenty, gentlemen, evidence, silly, admit, experience, fired, normal, talkin, lock, mission, memory, louis, fighting, notice, crap, wedding, promised, ground, idiot, orders, marriage, guns, glass, impossible, heaven, knock, spent, neck, wondering, green, animal, hole, press, drugs, nuts, position, broken, names, asleep, jerry, acting, feels, visit, plans, boyfriend, smoke, paris, wind, tells, gimme, holding, cross, sheriff, walked, mention, judge, code, writing, double, brothers, keeps, pardon, fellow, fell, closed, lovely, angry, cute, percent, surprised, charles, agree, bathroom, correct, address, ridiculous, summer, andy, rules, tommy, group, account, note, learned, colonel, pulled, sing, laugh, proud, sleeping, area, built, jump, upstairs, difficult, river, bobby, dirty, breakfast, bridge, betty, locked, amazing, north, alex, definitely, plus, feelings, accept, kick, worst, grace, gettin, wild, stories, steal, seriously, file, relationship, advice, nature, places, waste, contact, spot, apart, knowing, stole, beach, favorite, loose, level, song, faith, risk, played, eating, foot, patient, witness, turns, washington, action, build, obviously, begin, split, crew, command, games, decide, tight, nurse, keeping, bird, form, runs, copy, scene, jeffrey, arrest, complete, taste, consider, insane, teeth, shoes, henry, career, sooner, monster, devil, hall, innocent, showed, study, gift, weekend, heavy, keys, greatest, comin, destroy, danger, track, raise, suddenly, hanging, bruce, carl, california, apologize, concerned, blind, program, medical, chicken, sweetheart, drinking, forward, seventy, willing, shop, guard, legs, suspect, professor, admiral, data, ticket, camp, tree, goodnight, paying, burn, losing, possibly, dunno, television, senator, trick, murdered, dropped, extra, credit, starts, warm, stone, sold, hiding, meaning, taught, marty, cheap, lately, simply, science, lookin, following, harold, queen, majesty, jeff, corner, cars, heads, training, seat, duty, noticed, helped, bear, enemy, discuss, responsible, trial, dave)
topics: Array[Array[(String, Double)]] = Array(Array((right,0.002368539995607174), (love,0.0019026093436816463), (just,0.001739005396343051), (okay,0.001493567868602809), (know,0.0011919944841106388)), Array((like,0.012255569993473736), (just,0.007532527227834193), (come,0.007114873840600518), (know,0.006960825682483897), (think,0.006460380113586568)), Array((know,0.017593342399778864), (yeah,0.01729763439457538), (gonna,0.014297985209693677), (just,0.009395640487800467), (tell,0.007112117826339655)), Array((just,0.002310885836348927), (know,0.0020049203493508585), (better,0.001839601963450054), (like,0.0016545385663972387), (right,0.001505081787498549)), Array((know,0.012396058201765845), (didn,0.004786910731106122), (like,0.004783067030382327), (right,0.003733205551673614), (just,0.0028592628116592403)), Array((just,0.0028236500929191208), (know,0.0026011344347436015), (going,0.0015951009390631876), (didn,0.001385667983895007), (wait,0.001275555813151892)), Array((going,0.00275337137203844), (right,0.001685679960504387), (just,0.0015380845174617235), (know,0.0014818062892167352), (captain,0.0013896743515293423)), Array((going,0.011956735401221285), (just,0.006541063462593452), (know,0.005428932374204778), (think,0.004308569608730405), (believe,0.003696595226603709)), Array((think,0.0019959039820595533), (sorry,0.00198077299794292), (know,0.0016723315231586236), (shit,0.0015606901977245095), (right,0.0013015271817698212)), Array((know,0.003615862714921936), (said,0.001961114693915351), (sorry,0.0018595382287745752), (like,0.0017819242854891695), (think,0.0016468683030306027)), Array((time,0.008784671423019166), (want,0.00282365356227211), (sure,0.0024833597381016476), (know,0.0019777615447230884), (right,0.0016576304456760946)), Array((just,0.0021068918389201634), (like,0.0020497480766035994), (know,0.002022347553873645), (want,0.0019500819941038825), (said,0.001503771370040063)), Array((look,0.00433587608823225), (think,0.0025833796049907604), (know,0.002007970741805987), (going,0.0016840410422251017), (just,0.0010661551551733228)), Array((know,0.0020279945673448915), (come,0.0019980250335794405), (think,0.0012733121858788797), (going,0.001192108885417234), (okay,0.001186180285931844)), Array((like,0.004262090436242644), (right,0.0021537790725358777), (just,0.0013683197398457016), (know,0.0010911699327713488), (look,0.0010869000557749361)), Array((come,0.004769396664496132), (know,0.0026229974920448534), (like,0.0021612642420959253), (just,0.0013228057897488347), (right,0.001171812635848879)), Array((know,0.025323543461007635), (just,0.018361261941348715), (like,0.01574431601713426), (want,0.014855701536091734), (think,0.011957607420818889)), Array((like,0.004346004035796333), (know,0.0022903208899377127), (just,0.002008680613491114), (little,0.0019547134832950414), (maybe,0.0017287784612649724)), Array((know,0.003217184151682409), (think,0.003063734585623867), (just,0.0018328245079520728), (want,0.0017709019452594528), (like,0.0016903614729120188)), Array((hello,0.008911727886543675), (stop,0.0025143616929346174), (just,0.0023958078165974795), (like,0.00184251815055585), (come,0.0018199130672157007)))

Step 9. Create LDA model with Expectation Maximization

Let's try creating an LDA model with Expectation Maximization on the data that has been refiltered for additional stopwords. We will also increase MaxIterations here to 100 to see if that improves results. See:

import org.apache.spark.mllib.clustering.EMLDAOptimizer

// Set LDA parameters
val em_lda = new LDA()
.setOptimizer(new EMLDAOptimizer())
.setK(numTopics)
.setMaxIterations(100)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
import org.apache.spark.mllib.clustering.EMLDAOptimizer
em_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@7c84d0ae
val em_ldaModel = em_lda.run(new_lda_countVector) // takes a long long time 22 minutes
em_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.DistributedLDAModel@188f58bf
import org.apache.spark.mllib.clustering.DistributedLDAModel;
val em_DldaModel = em_ldaModel.asInstanceOf[DistributedLDAModel]
import org.apache.spark.mllib.clustering.DistributedLDAModel
em_DldaModel: org.apache.spark.mllib.clustering.DistributedLDAModel = org.apache.spark.mllib.clustering.DistributedLDAModel@188f58bf
val top10ConversationsPerTopic = em_DldaModel.topDocumentsPerTopic(10)
top10ConversationsPerTopic: Array[(Array[Long], Array[Double])] = Array((Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.031196200176884056, 0.020282154018599348, 0.01099645315549, 0.01099645315549)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.03471449892991739, 0.022506359024306477, 0.0112575667750105, 0.0112575667750105)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02390214852968437, 0.01563567947724491, 0.01037864738296468, 0.01037864738296468)), (Array(69318, 15221, 15149, 23167, 59606, 51632, 51639, 64470, 67338, 66968),Array(0.9999514001066685, 0.999945172626603, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05027652950865874, 0.032180017393406625, 0.011630224445618545, 0.011630224445618545)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.04313069897321676, 0.027782187731151566, 0.01176138814023006, 0.01176138814023006)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.046744086701796375, 0.030009654606021424, 0.011639894189919175, 0.011639894189919175)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04630740194891603, 0.02973828460805704, 0.011613267488918038, 0.011613267488918038)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02856137645845995, 0.01860910369309368, 0.010781174428638705, 0.010781174428638705)), (Array(39677, 39693, 39680, 39674, 39682, 39679, 39681, 39676, 41932, 41967),Array(0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05065478074966324, 0.032424728159182487, 0.011804021891259129, 0.011804021891259129)), (Array(39677, 39693, 39680, 39674, 39682, 39679, 39681, 39676, 41932, 41967),Array(0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.054382561204063574, 0.03470709039767808, 0.011833044488991173, 0.011833044488991173)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.026914567318365282, 0.017564142155031864, 0.010769649833867787, 0.010769649833867787)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.046074356990568124, 0.029584640688217628, 0.011466200232752964, 0.011466200232752964)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.055741511067049866, 0.03555776746162745, 0.01211237486139149, 0.01211237486139149)), (Array(39681, 39674, 39677, 39693, 39680, 39682, 39679, 39676, 41932, 41967),Array(0.06048967215526035, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.03841642394224334, 0.011870824949431247, 0.011870824949431247)), (Array(39681, 39679, 39677, 39693, 39680, 39674, 39682, 39676, 41932, 41967),Array(0.06567035036792095, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.041559067084071775, 0.012094068526188358, 0.012094068526188358)), (Array(39681, 39674, 39677, 39693, 39680, 39682, 39679, 39676, 41932, 41967),Array(0.07103855727399273, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.04473147169952923, 0.011812341727214322, 0.011812341727214322)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04327692350658171, 0.027860179926002853, 0.01152922335209424, 0.01152922335209424)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04316378247947061, 0.02777238708460723, 0.011237658307104376, 0.011237658307104376)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.0463101731327656, 0.02972951504690979, 0.011452462524389802, 0.011452462524389802)))
top10ConversationsPerTopic.length // number of topics
res52: Int = 20
//em_DldaModel.topicDistributions.take(10).foreach(println)

Note that the EMLDAOptimizer produces a DistributedLDAModel, which stores not only the inferred topics but also the full training corpus and topic distributions for each document in the training corpus.

val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(6435, 9153, 2611, 9555, 9235),Array(1.0844350865928232E-5, 1.4037356622456141E-6, 1.0198257636937534E-6, 1.010016392533973E-6, 9.877489659219E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.2201894817101623E-5, 1.4560010186049552E-6, 1.0547580487281058E-6, 1.0446104695648421E-6, 1.0214202904824573E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(8.080320102037276E-6, 1.2828806625265042E-6, 9.387148884503143E-7, 9.296944883594565E-7, 9.095512260026888E-7)), (Array(0, 1, 2, 3, 4),Array(0.4097048012129488, 0.2966641691130405, 0.28104437242573427, 0.2068481221090779, 0.20178462784115517)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.7791420865642426E-5, 1.5285401934315644E-6, 1.1022151610359566E-6, 1.0916092052333647E-6, 1.0671154286074535E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5488156532652564E-5, 1.5613578155095174E-6, 1.1250530213722066E-6, 1.1142275765190935E-6, 1.0891766415036671E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.66201985348282E-5, 1.5337088341752489E-6, 1.1062252459821718E-6, 1.0955808549686414E-6, 1.0710096202234095E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.6434785283305463E-5, 1.527062738898831E-6, 1.1015632294086975E-6, 1.0909636379478556E-6, 1.066504587082138E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(9.82890555944203E-6, 1.360381381982805E-6, 9.90695338703216E-7, 9.811686105969582E-7, 9.596620143926599E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.8098274080649888E-5, 1.5662560052135424E-6, 1.127571968783498E-6, 1.1167221871321394E-6, 1.0915664277968502E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.9443267750173392E-5, 1.5746049595955017E-6, 1.1333735056120856E-6, 1.1224679386855895E-6, 1.0971718558358495E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(9.292996004992278E-6, 1.3619125930485615E-6, 9.924672219451632E-7, 9.82924355173023E-7, 9.614096002911668E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.619330796598465E-5, 1.4932367796221485E-6, 1.0785114269963956E-6, 1.06813378362302E-6, 1.0442595139466752E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.0195445781462442E-5, 1.6338598744234947E-6, 1.1726861776132844E-6, 1.1614034519421386E-6, 1.1350541791534873E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.1543159186970775E-5, 1.5791785506830092E-6, 1.1358076217376717E-6, 1.1248786573437884E-6, 1.099486352793341E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.3565018803229148E-5, 1.6252544688003071E-6, 1.16608206417593E-6, 1.1548627950846766E-6, 1.1286452359926982E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.498926755354901E-5, 1.5618937315237142E-6, 1.1234358831022108E-6, 1.1126257210374892E-6, 1.0875181953216021E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5342892698391062E-5, 1.5117065677915513E-6, 1.0917779017440848E-6, 1.0812727863583168E-6, 1.057095929328646E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5018034022313325E-5, 1.4466343222454145E-6, 1.04735014561389E-6, 1.0372732437703543E-6, 1.0142213705569144E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.627670929533595E-5, 1.4917359134556584E-6, 1.0776961757775105E-6, 1.067326467379095E-6, 1.043483836251971E-6)))
val vocabList = vectorizer.vocabulary
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, hear, stop, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, problem, cause, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, supposed, makes, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, christ, waiting, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, died, hands, power, stupid, started, shouldn, months, boys, city, sense, dinner, running, hour, shoot, fight, drive, speak, george, ship, living, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, safe, moment, outside, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, promise, lives, till, sound, weren, save, poor, cool, shall, asking, plan, king, bitch, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, company, food, tired, lucky, pull, wonderful, touch, looked, thinks, state, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, hotel, explain, kidding, worse, past, outta, general, felt, drop, unless, throw, interested, hang, certainly, absolutely, earth, loved, dark, wonder, accident, seeing, turned, clock, simple, doin, date, sweet, meeting, clean, sign, feet, handle, music, report, giving, army, fucked, cops, charlie, smart, yesterday, information, fall, fault, bank, class, month, blow, swear, caught, major, paul, road, talked, choice, plane, boss, david, paid, wear, american, worried, lord, paper, goodbye, clothes, ones, terrible, strange, given, mistake, finish, kept, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, figured, future, rose, building, mama, michael, early, agent, kinda, watching, private, trip, record, certain, busy, jimmy, broke, sake, longer, store, boat, stick, finally, born, evening, sitting, bucks, ought, chief, lying, history, kiss, honor, darling, lunch, favor, fool, uncle, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, problems, welcome, nick, wake, honest, radio, dick, cash, dance, dude, james, bout, floor, weird, court, calls, jail, drunk, window, involved, johnny, officer, needed, asshole, situation, spend, books, relax, pain, service, grand, dangerous, letter, security, stopped, offer, realize, table, bastard, message, instead, killer, jake, deep, nervous, somethin, pass, evil, english, bought, short, step, ring, picked, likes, machine, eddie, voice, upset, forgot, carry, lived, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, pleasure, doubt, lawyer, station, gotten, suit, bother, prove, return, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, attack, appreciate, force, church, college, hungry, standing, present, dying, prison, missing, charge, board, truck, public, calm, gold, staying, ball, hardly, hadn, missed, lead, island, government, horse, cover, french, reach, joke, fish, star, mike, surprise, america, moved, soul, dress, seconds, club, self, putting, movies, lots, cost, listening, price, saved, smell, mark, peace, dreams, entire, crime, gives, usually, single, department, holy, beer, west, protect, stuck, wall, nose, ways, teach, forever, grow, train, type, awful, rock, detective, billy, walking, dumb, papers, beginning, planet, folks, park, attention, birthday, hide, card, master, share, reading, test, starting, lieutenant, field, partner, enjoy, twice, film, dollar, bomb, mess, blame, south, loves, girlfriend, round, records, using, plenty, especially, gentlemen, evidence, silly, experience, admit, fired, normal, talkin, mission, louis, memory, fighting, lock, notice, crap, wedding, promised, marriage, ground, guns, glass, idiot, orders, impossible, heaven, knock, hole, neck, animal, spent, green, wondering, nuts, press, drugs, broken, position, names, asleep, jerry, visit, boyfriend, acting, feels, plans, paris, smoke, tells, wind, cross, holding, sheriff, gimme, walked, mention, writing, double, brothers, code, judge, pardon, keeps, fellow, fell, closed, lovely, angry, cute, charles, surprised, percent, correct, bathroom, agree, address, andy, ridiculous, summer, tommy, rules, group, account, note, pulled, sleeping, sing, learned, proud, laugh, colonel, upstairs, river, difficult, built, jump, area, dirty, betty, bridge, breakfast, bobby, locked, amazing, north, feelings, alex, plus, definitely, worst, accept, kick, seriously, grace, steal, wild, stories, file, gettin, relationship, advice, nature, contact, spot, places, waste, knowing, beach, stole, apart, favorite, faith, level, loose, risk, song, eating, foot, played, patient, washington, turns, witness, action, build, obviously, begin, split, crew, command, games, tight, decide, nurse, keeping, runs, form, bird, copy, insane, complete, arrest, consider, taste, scene, jeffrey, teeth, shoes, career, henry, sooner, devil, monster, showed, weekend, gift, innocent, study, heavy, hall, comin, danger, greatest, track, keys, raise, destroy, concerned, program, carl, blind, apologize, suddenly, hanging, bruce, california, chicken, seventy, forward, drinking, sweetheart, medical, suspect, admiral, guard, shop, professor, legs, willing, camp, data, ticket, tree, goodnight, television, losing, senator, murdered, burn, dunno, paying, possibly, trick, dropped, credit, extra, starts, warm, hiding, meaning, sold, stone, taught, marty, lately, cheap, lookin, science, simply, jeff, corner, harold, following, majesty, queen, duty, cars, training, heads, seat, discuss, bear, enemy, helped, noticed, common, screw, dave)
vocabList.size
res32: Int = 10000
val topics = topicIndices.map { case (terms, termWeights) =>
  terms.map(vocabList(_)).zip(termWeights)
}
topics: Array[Array[(String, Double)]] = Array(Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)), Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)), Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)), Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)), Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)), Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)), Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)), Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)), Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)), Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)), Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)), Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)), Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)), Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)), Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)), Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)), Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)), Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)), Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)), Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)))
vocabList(47) // 47 is the index of the term 'university' or the first term in topics - this may change due to randomness in algorithm
res33: String = doesn

This is just doing it all at once.

val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
  terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
  println(s"TOPIC $i")
  topic.foreach { case (term, weight) => println(s"$term\t$weight") }
  println(s"==========")
}
20 topics:
TOPIC 0
just	0.030515134931284552
like	0.02463563559747823
want	0.022529385381465025
damn	0.02094828832824297
going	0.0203407289886203
==========
TOPIC 1
yeah	0.10787301090151602
look	0.0756831002291994
know	0.04815746564274915
wait	0.03897182014529944
night	0.0341458394828345
==========
TOPIC 2
gonna	0.08118584492034046
money	0.051736711600637544
shit	0.04620430294274594
fuck	0.0399843125556081
kill	0.03672740843080258
==========
TOPIC 3
people	0.020091372023286612
know	0.018613400462887356
work	0.016775643603287843
does	0.015522555458447744
think	0.012161168331925723
==========
TOPIC 4
know	0.031956573561538214
just	0.030674598809934856
want	0.027663491240851962
tell	0.025727217382788027
right	0.02300853167338119
==========
TOPIC 5
love	0.05932570200934131
father	0.030080735900045442
life	0.01769248067468245
true	0.016281752071881345
young	0.014927950883812253
==========
TOPIC 6
remember	0.03998401809663685
went	0.01737965538107633
lost	0.016916065536574213
called	0.016443441316683228
story	0.014849882671062261
==========
TOPIC 7
house	0.028911209424810257
miss	0.025669944694943093
right	0.02091105252727788
family	0.017862939987512365
important	0.013959164390834044
==========
TOPIC 8
saying	0.022939827090645636
know	0.021335083902970984
idea	0.017628999871937747
business	0.017302568063786224
police	0.012284217866942303
==========
TOPIC 9
know	0.051876601466269136
like	0.03828159069993671
maybe	0.03754385940676905
just	0.031938551661426284
want	0.02876693222824349
==========
TOPIC 10
years	0.032537676027398765
going	0.030596831997667568
case	0.02049555392502822
doctor	0.018671171294737107
working	0.017672067172167016
==========
TOPIC 11
stuff	0.02236582778896705
school	0.020057798194969816
john	0.017134198006217606
week	0.017075852415410653
thousand	0.017013413435021035
==========
TOPIC 12
little	0.08663446368316245
girl	0.035120377589734936
like	0.02992080326340266
woman	0.0240813719635157
baby	0.022471517953608963
==========
TOPIC 13
know	0.0283115823590395
leave	0.02744935904744228
time	0.02050833156294194
want	0.020124145131863225
just	0.019466336438890477
==========
TOPIC 14
didn	0.08220031921979461
like	0.05062323326717784
real	0.03087838046777391
guess	0.02452989702353384
says	0.022815035397008333
==========
TOPIC 15
minutes	0.018541518543996716
time	0.014737962244588431
captain	0.012594614743931537
thirty	0.01193707771669708
ship	0.011260576815409516
==========
TOPIC 16
okay	0.08153575328080886
just	0.050004142902999975
right	0.03438984898476042
know	0.02821327795933634
home	0.023397063860326372
==========
TOPIC 17
country	0.011270500385627474
power	0.010428408353623762
president	0.009392162067926028
fight	0.00799742811584178
possible	0.007597974486019279
==========
TOPIC 18
know	0.09541058020800194
think	0.0698707939786508
really	0.06881812755565207
mean	0.02909700228968688
just	0.028699687473471538
==========
TOPIC 19
dead	0.03833642117149438
like	0.017873711992106994
hand	0.015280854355409379
white	0.013718491413582671
blood	0.012699265888344448
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 3, 135, 6),Array(0.030515134931284552, 0.02463563559747823, 0.022529385381465025, 0.02094828832824297, 0.0203407289886203)), (Array(8, 12, 0, 57, 32),Array(0.10787301090151602, 0.0756831002291994, 0.04815746564274915, 0.03897182014529944, 0.0341458394828345)), (Array(20, 35, 42, 51, 58),Array(0.08118584492034046, 0.051736711600637544, 0.04620430294274594, 0.0399843125556081, 0.03672740843080258)), (Array(22, 0, 34, 43, 4),Array(0.020091372023286612, 0.018613400462887356, 0.016775643603287843, 0.015522555458447744, 0.012161168331925723)), (Array(0, 1, 3, 9, 5),Array(0.031956573561538214, 0.030674598809934856, 0.027663491240851962, 0.025727217382788027, 0.02300853167338119)), (Array(27, 74, 31, 168, 202),Array(0.05932570200934131, 0.030080735900045442, 0.01769248067468245, 0.016281752071881345, 0.014927950883812253)), (Array(53, 92, 180, 113, 166),Array(0.03998401809663685, 0.01737965538107633, 0.016916065536574213, 0.016443441316683228, 0.014849882671062261)), (Array(78, 110, 5, 171, 232),Array(0.028911209424810257, 0.025669944694943093, 0.02091105252727788, 0.017862939987512365, 0.013959164390834044)), (Array(119, 0, 107, 106, 219),Array(0.022939827090645636, 0.021335083902970984, 0.017628999871937747, 0.017302568063786224, 0.012284217866942303)), (Array(0, 2, 24, 1, 3),Array(0.051876601466269136, 0.03828159069993671, 0.03754385940676905, 0.031938551661426284, 0.02876693222824349)), (Array(41, 6, 140, 162, 177),Array(0.032537676027398765, 0.030596831997667568, 0.02049555392502822, 0.018671171294737107, 0.017672067172167016)), (Array(118, 130, 181, 174, 170),Array(0.02236582778896705, 0.020057798194969816, 0.017134198006217606, 0.017075852415410653, 0.017013413435021035)), (Array(18, 62, 2, 114, 122),Array(0.08663446368316245, 0.035120377589734936, 0.02992080326340266, 0.0240813719635157, 0.022471517953608963)), (Array(0, 64, 11, 3, 1),Array(0.0283115823590395, 0.02744935904744228, 0.02050833156294194, 0.020124145131863225, 0.019466336438890477)), (Array(13, 2, 67, 59, 111),Array(0.08220031921979461, 0.05062323326717784, 0.03087838046777391, 0.02452989702353384, 0.022815035397008333)), (Array(158, 11, 233, 274, 295),Array(0.018541518543996716, 0.014737962244588431, 0.012594614743931537, 0.01193707771669708, 0.011260576815409516)), (Array(16, 1, 5, 0, 49),Array(0.08153575328080886, 0.050004142902999975, 0.03438984898476042, 0.02821327795933634, 0.023397063860326372)), (Array(257, 279, 313, 291, 351),Array(0.011270500385627474, 0.010428408353623762, 0.009392162067926028, 0.00799742811584178, 0.007597974486019279)), (Array(0, 4, 17, 14, 1),Array(0.09541058020800194, 0.0698707939786508, 0.06881812755565207, 0.02909700228968688, 0.028699687473471538)), (Array(54, 2, 198, 248, 266),Array(0.03833642117149438, 0.017873711992106994, 0.015280854355409379, 0.013718491413582671, 0.012699265888344448)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, hear, stop, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, problem, cause, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, supposed, makes, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, christ, waiting, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, died, hands, power, stupid, started, shouldn, months, boys, city, sense, dinner, running, hour, shoot, fight, drive, speak, george, ship, living, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, safe, moment, outside, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, promise, lives, till, sound, weren, save, poor, cool, shall, asking, plan, king, bitch, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, company, food, tired, lucky, pull, wonderful, touch, looked, thinks, state, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, hotel, explain, kidding, worse, past, outta, general, felt, drop, unless, throw, interested, hang, certainly, absolutely, earth, loved, dark, wonder, accident, seeing, turned, clock, simple, doin, date, sweet, meeting, clean, sign, feet, handle, music, report, giving, army, fucked, cops, charlie, smart, yesterday, information, fall, fault, bank, class, month, blow, swear, caught, major, paul, road, talked, choice, plane, boss, david, paid, wear, american, worried, lord, paper, goodbye, clothes, ones, terrible, strange, given, mistake, finish, kept, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, figured, future, rose, building, mama, michael, early, agent, kinda, watching, private, trip, record, certain, busy, jimmy, broke, sake, longer, store, boat, stick, finally, born, evening, sitting, bucks, ought, chief, lying, history, kiss, honor, darling, lunch, favor, fool, uncle, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, problems, welcome, nick, wake, honest, radio, dick, cash, dance, dude, james, bout, floor, weird, court, calls, jail, drunk, window, involved, johnny, officer, needed, asshole, situation, spend, books, relax, pain, service, grand, dangerous, letter, security, stopped, offer, realize, table, bastard, message, instead, killer, jake, deep, nervous, somethin, pass, evil, english, bought, short, step, ring, picked, likes, machine, eddie, voice, upset, forgot, carry, lived, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, pleasure, doubt, lawyer, station, gotten, suit, bother, prove, return, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, attack, appreciate, force, church, college, hungry, standing, present, dying, prison, missing, charge, board, truck, public, calm, gold, staying, ball, hardly, hadn, missed, lead, island, government, horse, cover, french, reach, joke, fish, star, mike, surprise, america, moved, soul, dress, seconds, club, self, putting, movies, lots, cost, listening, price, saved, smell, mark, peace, dreams, entire, crime, gives, usually, single, department, holy, beer, west, protect, stuck, wall, nose, ways, teach, forever, grow, train, type, awful, rock, detective, billy, walking, dumb, papers, beginning, planet, folks, park, attention, birthday, hide, card, master, share, reading, test, starting, lieutenant, field, partner, enjoy, twice, film, dollar, bomb, mess, blame, south, loves, girlfriend, round, records, using, plenty, especially, gentlemen, evidence, silly, experience, admit, fired, normal, talkin, mission, louis, memory, fighting, lock, notice, crap, wedding, promised, marriage, ground, guns, glass, idiot, orders, impossible, heaven, knock, hole, neck, animal, spent, green, wondering, nuts, press, drugs, broken, position, names, asleep, jerry, visit, boyfriend, acting, feels, plans, paris, smoke, tells, wind, cross, holding, sheriff, gimme, walked, mention, writing, double, brothers, code, judge, pardon, keeps, fellow, fell, closed, lovely, angry, cute, charles, surprised, percent, correct, bathroom, agree, address, andy, ridiculous, summer, tommy, rules, group, account, note, pulled, sleeping, sing, learned, proud, laugh, colonel, upstairs, river, difficult, built, jump, area, dirty, betty, bridge, breakfast, bobby, locked, amazing, north, feelings, alex, plus, definitely, worst, accept, kick, seriously, grace, steal, wild, stories, file, gettin, relationship, advice, nature, contact, spot, places, waste, knowing, beach, stole, apart, favorite, faith, level, loose, risk, song, eating, foot, played, patient, washington, turns, witness, action, build, obviously, begin, split, crew, command, games, tight, decide, nurse, keeping, runs, form, bird, copy, insane, complete, arrest, consider, taste, scene, jeffrey, teeth, shoes, career, henry, sooner, devil, monster, showed, weekend, gift, innocent, study, heavy, hall, comin, danger, greatest, track, keys, raise, destroy, concerned, program, carl, blind, apologize, suddenly, hanging, bruce, california, chicken, seventy, forward, drinking, sweetheart, medical, suspect, admiral, guard, shop, professor, legs, willing, camp, data, ticket, tree, goodnight, television, losing, senator, murdered, burn, dunno, paying, possibly, trick, dropped, credit, extra, starts, warm, hiding, meaning, sold, stone, taught, marty, lately, cheap, lookin, science, simply, jeff, corner, harold, following, majesty, queen, duty, cars, training, heads, seat, discuss, bear, enemy, helped, noticed, common, screw, dave)
topics: Array[Array[(String, Double)]] = Array(Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)), Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)), Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)), Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)), Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)), Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)), Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)), Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)), Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)), Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)), Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)), Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)), Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)), Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)), Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)), Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)), Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)), Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)), Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)), Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)))
top10ConversationsPerTopic(2)
res54: (Array[Long], Array[Double]) = (Array(22243, 39967, 18136, 18149, 59043, 61513, 34087, 75874, 66270, 68876),Array(0.9986758340945384, 0.99866200816902, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165))
top10ConversationsPerTopic(2)._1
res55: Array[Long] = Array(22243, 39967, 18136, 18149, 59043, 61513, 34087, 75874, 66270, 68876)
val scenesForTopic2 = sc.parallelize(top10ConversationsPerTopic(2)._1).toDF("id")
scenesForTopic2: org.apache.spark.sql.DataFrame = [id: bigint]
display(scenesForTopic2.join(corpusDF,"id"))
id corpus movieTitle movieYear
22243.0 Fuck him. :-()-: Don't. :-()-: Fuck her too. panic room 2002
59043.0 Are you ok? :-()-: Fuck no. magnolia 1999
66270.0 Hey now... what the fuck... ? :-()-: Again. red white black & blue 2006
75874.0 What about Moliere? :-()-: Fuck off. the beach 2000/I
68876.0 What the fuck is that? :-()-: A switchblade. seven 1979
34087.0 Fuck me! Yes! :-()-: Uh... american pie 1999
61513.0 What the fuck is that?! :-()-: Screamer. arcade 1993
18136.0 What the fuck was that about? :-()-: She was jonesing for me. made 2001
18149.0 C'mon... :-()-: Fuck... made 2001
39967.0 Shit, shit, shit... :-()-: You're almost there, you can do it -- can do -- can do. broadcast news 1987
sc.parallelize(top10ConversationsPerTopic(2)._1).toDF("id").join(corpusDF,"id").show(10,false)
+-----+----------------------------------------------------------------------------------+----------------------+---------+
|id   |corpus                                                                            |movieTitle            |movieYear|
+-----+----------------------------------------------------------------------------------+----------------------+---------+
|22243|Fuck him. :-()-: Don't. :-()-: Fuck her too.                                      |panic room            |2002     |
|59043|Are you ok? :-()-: Fuck no.                                                       |magnolia              |1999     |
|66270|Hey now... what the fuck... ? :-()-: Again.                                       |red white black & blue|2006     |
|75874|What about Moliere? :-()-: Fuck off.                                              |the beach             |2000/I   |
|68876|What the fuck is that? :-()-: A switchblade.                                      |seven                 |1979     |
|34087|Fuck me!  Yes! :-()-: Uh...                                                       |american pie          |1999     |
|61513|What the fuck is that?! :-()-: Screamer.                                          |arcade                |1993     |
|18136|What the fuck was that about? :-()-: She was jonesing for me.                     |made                  |2001     |
|18149|C'mon... :-()-: Fuck...                                                           |made                  |2001     |
|39967|Shit, shit, shit... :-()-: You're almost there, you can do it -- can do -- can do.|broadcast news        |1987     |
+-----+----------------------------------------------------------------------------------+----------------------+---------+
sc.parallelize(top10ConversationsPerTopic(5)._1).toDF("id").join(corpusDF,"id").show(10,false)
+-----+---------------------------------------------------------+-----------------+---------+
|id   |corpus                                                   |movieTitle       |movieYear|
+-----+---------------------------------------------------------+-----------------+---------+
|68250|I love you man :-()-: I love you too.                    |say anything...  |1989     |
|31256|I love you. :-()-: I love you.                           |total recall     |1990     |
|868  |I love you. :-()-: I love you.                           |8mm              |1999     |
|17285|Do me. :-()-: I love you. :-()-: I love you.             |little nicky     |2000     |
|56529|Why do you love me? :-()-: Why do you love me?           |jerry maguire    |1996     |
|67529|I love you, too. :-()-: I love you.  I love you.         |runaway bride    |1999     |
|82132|Why did you say that? :-()-: Say what? :-()-: I love you.|willow           |1988     |
|50163|I love you, Bud. :-()-: I love you more.                 |frequency        |2000     |
|39173|I love you. :-()-: I love you too, Dad.                  |body of evidence |1993     |
|57385|Yes? :-()-: I love you...                                |kramer vs. kramer|1979     |
+-----+---------------------------------------------------------+-----------------+---------+
corpusDF.show(5)
+-----+--------------------+------------+---------+
|   id|              corpus|  movieTitle|movieYear|
+-----+--------------------+------------+---------+
|17668|This would be fun...|lost horizon|     1937|
|17598|Cave, eh? Where? ...|lost horizon|     1937|
|17663|Something grand a...|lost horizon|     1937|
|17593|You see? You get ...|lost horizon|     1937|
|17658|Let me up! Let me...|lost horizon|     1937|
+-----+--------------------+------------+---------+
only showing top 5 rows

We've managed to get some good results here. For example, we can easily infer that Topic 2 is about space, Topic 3 is about israel, etc.

We still get some ambiguous results like Topic 0.

To improve our results further, we could employ some of the below methods:

  • Refilter data for additional data-specific stopwords
  • Use Stemming or Lemmatization to preprocess data
  • Experiment with a smaller number of topics, since some of these topics in the 20 Newsgroups are pretty similar
  • Increase model's MaxIterations

Visualize Results

We will try visualizing the results obtained from the EM LDA model with a d3 bubble chart.

// Zip topic terms with topic IDs
val termArray = topics.zipWithIndex
termArray: Array[(Array[(String, Double)], Int)] = Array((Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)),0), (Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)),1), (Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)),2), (Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)),3), (Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)),4), (Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)),5), (Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)),6), (Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)),7), (Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)),8), (Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)),9), (Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)),10), (Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)),11), (Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)),12), (Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)),13), (Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)),14), (Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)),15), (Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)),16), (Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)),17), (Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)),18), (Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)),19))
// Transform data into the form (term, probability, topicId)
val termRDD = sc.parallelize(termArray)
val termRDD2 =termRDD.flatMap( (x: (Array[(String, Double)], Int)) => {
  val arrayOfTuple = x._1
  val topicId = x._2
  arrayOfTuple.map(el => (el._1, el._2, topicId))
})
termRDD: org.apache.spark.rdd.RDD[(Array[(String, Double)], Int)] = ParallelCollectionRDD[3066] at parallelize at <console>:109
termRDD2: org.apache.spark.rdd.RDD[(String, Double, Int)] = MapPartitionsRDD[3067] at flatMap at <console>:110
// Create DF with proper column names
val termDF = termRDD2.toDF.withColumnRenamed("_1", "term").withColumnRenamed("_2", "probability").withColumnRenamed("_3", "topicId")
termDF: org.apache.spark.sql.DataFrame = [term: string, probability: double, topicId: int]
display(termDF)
term probability topicId
just 3.0515134931284552e-2 0.0
like 2.463563559747823e-2 0.0
want 2.2529385381465025e-2 0.0
damn 2.094828832824297e-2 0.0
going 2.03407289886203e-2 0.0
yeah 0.10787301090151602 1.0
look 7.56831002291994e-2 1.0
know 4.815746564274915e-2 1.0
wait 3.897182014529944e-2 1.0
night 3.41458394828345e-2 1.0
gonna 8.118584492034046e-2 2.0
money 5.1736711600637544e-2 2.0
shit 4.620430294274594e-2 2.0
fuck 3.99843125556081e-2 2.0
kill 3.672740843080258e-2 2.0
people 2.0091372023286612e-2 3.0
know 1.8613400462887356e-2 3.0
work 1.6775643603287843e-2 3.0
does 1.5522555458447744e-2 3.0
think 1.2161168331925723e-2 3.0
know 3.1956573561538214e-2 4.0
just 3.0674598809934856e-2 4.0
want 2.7663491240851962e-2 4.0
tell 2.5727217382788027e-2 4.0
right 2.300853167338119e-2 4.0
love 5.932570200934131e-2 5.0
father 3.0080735900045442e-2 5.0
life 1.769248067468245e-2 5.0
true 1.6281752071881345e-2 5.0
young 1.4927950883812253e-2 5.0
remember 3.998401809663685e-2 6.0
went 1.737965538107633e-2 6.0
lost 1.6916065536574213e-2 6.0
called 1.6443441316683228e-2 6.0
story 1.4849882671062261e-2 6.0
house 2.8911209424810257e-2 7.0
miss 2.5669944694943093e-2 7.0
right 2.091105252727788e-2 7.0
family 1.7862939987512365e-2 7.0
important 1.3959164390834044e-2 7.0
saying 2.2939827090645636e-2 8.0
know 2.1335083902970984e-2 8.0
idea 1.7628999871937747e-2 8.0
business 1.7302568063786224e-2 8.0
police 1.2284217866942303e-2 8.0
know 5.1876601466269136e-2 9.0
like 3.828159069993671e-2 9.0
maybe 3.754385940676905e-2 9.0
just 3.1938551661426284e-2 9.0
want 2.876693222824349e-2 9.0
years 3.2537676027398765e-2 10.0
going 3.0596831997667568e-2 10.0
case 2.049555392502822e-2 10.0
doctor 1.8671171294737107e-2 10.0
working 1.7672067172167016e-2 10.0
stuff 2.236582778896705e-2 11.0
school 2.0057798194969816e-2 11.0
john 1.7134198006217606e-2 11.0
week 1.7075852415410653e-2 11.0
thousand 1.7013413435021035e-2 11.0
little 8.663446368316245e-2 12.0
girl 3.5120377589734936e-2 12.0
like 2.992080326340266e-2 12.0
woman 2.40813719635157e-2 12.0
baby 2.2471517953608963e-2 12.0
know 2.83115823590395e-2 13.0
leave 2.744935904744228e-2 13.0
time 2.050833156294194e-2 13.0
want 2.0124145131863225e-2 13.0
just 1.9466336438890477e-2 13.0
didn 8.220031921979461e-2 14.0
like 5.062323326717784e-2 14.0
real 3.087838046777391e-2 14.0
guess 2.452989702353384e-2 14.0
says 2.2815035397008333e-2 14.0
minutes 1.8541518543996716e-2 15.0
time 1.4737962244588431e-2 15.0
captain 1.2594614743931537e-2 15.0
thirty 1.193707771669708e-2 15.0
ship 1.1260576815409516e-2 15.0
okay 8.153575328080886e-2 16.0
just 5.0004142902999975e-2 16.0
right 3.438984898476042e-2 16.0
know 2.821327795933634e-2 16.0
home 2.3397063860326372e-2 16.0
country 1.1270500385627474e-2 17.0
power 1.0428408353623762e-2 17.0
president 9.392162067926028e-3 17.0
fight 7.99742811584178e-3 17.0
possible 7.597974486019279e-3 17.0
know 9.541058020800194e-2 18.0
think 6.98707939786508e-2 18.0
really 6.881812755565207e-2 18.0
mean 2.909700228968688e-2 18.0
just 2.8699687473471538e-2 18.0
dead 3.833642117149438e-2 19.0
like 1.7873711992106994e-2 19.0
hand 1.5280854355409379e-2 19.0
white 1.3718491413582671e-2 19.0
blood 1.2699265888344448e-2 19.0

We will convert the DataFrame into a JSON format, which will be passed into d3.

// Create JSON data
val rawJson = termDF.toJSON.collect().mkString(",\n")
rawJson: String = 
{"term":"just","probability":0.030515134931284552,"topicId":0},
{"term":"like","probability":0.02463563559747823,"topicId":0},
{"term":"want","probability":0.022529385381465025,"topicId":0},
{"term":"damn","probability":0.02094828832824297,"topicId":0},
{"term":"going","probability":0.0203407289886203,"topicId":0},
{"term":"yeah","probability":0.10787301090151602,"topicId":1},
{"term":"look","probability":0.0756831002291994,"topicId":1},
{"term":"know","probability":0.04815746564274915,"topicId":1},
{"term":"wait","probability":0.03897182014529944,"topicId":1},
{"term":"night","probability":0.0341458394828345,"topicId":1},
{"term":"gonna","probability":0.08118584492034046,"topicId":2},
{"term":"money","probability":0.051736711600637544,"topicId":2},
{"term":"shit","probability":0.04620430294274594,"topicId":2},
{"term":"fuck","probability":0.0399843125556081,"topicId":2},
{"term":"kill","probability":0.03672740843080258,"topicId":2},
{"term":"people","probability":0.020091372023286612,"topicId":3},
{"term":"know","probability":0.018613400462887356,"topicId":3},
{"term":"work","probability":0.016775643603287843,"topicId":3},
{"term":"does","probability":0.015522555458447744,"topicId":3},
{"term":"think","probability":0.012161168331925723,"topicId":3},
{"term":"know","probability":0.031956573561538214,"topicId":4},
{"term":"just","probability":0.030674598809934856,"topicId":4},
{"term":"want","probability":0.027663491240851962,"topicId":4},
{"term":"tell","probability":0.025727217382788027,"topicId":4},
{"term":"right","probability":0.02300853167338119,"topicId":4},
{"term":"love","probability":0.05932570200934131,"topicId":5},
{"term":"father","probability":0.030080735900045442,"topicId":5},
{"term":"life","probability":0.01769248067468245,"topicId":5},
{"term":"true","probability":0.016281752071881345,"topicId":5},
{"term":"young","probability":0.014927950883812253,"topicId":5},
{"term":"remember","probability":0.03998401809663685,"topicId":6},
{"term":"went","probability":0.01737965538107633,"topicId":6},
{"term":"lost","probability":0.016916065536574213,"topicId":6},
{"term":"called","probability":0.016443441316683228,"topicId":6},
{"term":"story","probability":0.014849882671062261,"topicId":6},
{"term":"house","probability":0.028911209424810257,"topicId":7},
{"term":"miss","probability":0.025669944694943093,"topicId":7},
{"term":"right","probability":0.02091105252727788,"topicId":7},
{"term":"family","probability":0.017862939987512365,"topicId":7},
{"term":"important","probability":0.013959164390834044,"topicId":7},
{"term":"saying","probability":0.022939827090645636,"topicId":8},
{"term":"know","probability":0.021335083902970984,"topicId":8},
{"term":"idea","probability":0.017628999871937747,"topicId":8},
{"term":"business","probability":0.017302568063786224,"topicId":8},
{"term":"police","probability":0.012284217866942303,"topicId":8},
{"term":"know","probability":0.051876601466269136,"topicId":9},
{"term":"like","probability":0.03828159069993671,"topicId":9},
{"term":"maybe","probability":0.03754385940676905,"topicId":9},
{"term":"just","probability":0.031938551661426284,"topicId":9},
{"term":"want","probability":0.02876693222824349,"topicId":9},
{"term":"years","probability":0.032537676027398765,"topicId":10},
{"term":"going","probability":0.030596831997667568,"topicId":10},
{"term":"case","probability":0.02049555392502822,"topicId":10},
{"term":"doctor","probability":0.018671171294737107,"topicId":10},
{"term":"working","probability":0.017672067172167016,"topicId":10},
{"term":"stuff","probability":0.02236582778896705,"topicId":11},
{"term":"school","probability":0.020057798194969816,"topicId":11},
{"term":"john","probability":0.017134198006217606,"topicId":11},
{"term":"week","probability":0.017075852415410653,"topicId":11},
{"term":"thousand","probability":0.017013413435021035,"topicId":11},
{"term":"little","probability":0.08663446368316245,"topicId":12},
{"term":"girl","probability":0.035120377589734936,"topicId":12},
{"term":"like","probability":0.02992080326340266,"topicId":12},
{"term":"woman","probability":0.0240813719635157,"topicId":12},
{"term":"baby","probability":0.022471517953608963,"topicId":12},
{"term":"know","probability":0.0283115823590395,"topicId":13},
{"term":"leave","probability":0.02744935904744228,"topicId":13},
{"term":"time","probability":0.02050833156294194,"topicId":13},
{"term":"want","probability":0.020124145131863225,"topicId":13},
{"term":"just","probability":0.019466336438890477,"topicId":13},
{"term":"didn","probability":0.08220031921979461,"topicId":14},
{"term":"like","probability":0.05062323326717784,"topicId":14},
{"term":"real","probability":0.03087838046777391,"topicId":14},
{"term":"guess","probability":0.02452989702353384,"topicId":14},
{"term":"says","probability":0.022815035397008333,"topicId":14},
{"term":"minutes","probability":0.018541518543996716,"topicId":15},
{"term":"time","probability":0.014737962244588431,"topicId":15},
{"term":"captain","probability":0.012594614743931537,"topicId":15},
{"term":"thirty","probability":0.01193707771669708,"topicId":15},
{"term":"ship","probability":0.011260576815409516,"topicId":15},
{"term":"okay","probability":0.08153575328080886,"topicId":16},
{"term":"just","probability":0.050004142902999975,"topicId":16},
{"term":"right","probability":0.03438984898476042,"topicId":16},
{"term":"know","probability":0.02821327795933634,"topicId":16},
{"term":"home","probability":0.023397063860326372,"topicId":16},
{"term":"country","probability":0.011270500385627474,"topicId":17},
{"term":"power","probability":0.010428408353623762,"topicId":17},
{"term":"president","probability":0.009392162067926028,"topicId":17},
{"term":"fight","probability":0.00799742811584178,"topicId":17},
{"term":"possible","probability":0.007597974486019279,"topicId":17},
{"term":"know","probability":0.09541058020800194,"topicId":18},
{"term":"think","probability":0.0698707939786508,"topicId":18},
{"term":"really","probability":0.06881812755565207,"topicId":18},
{"term":"mean","probability":0.02909700228968688,"topicId":18},
{"term":"just","probability":0.028699687473471538,"topicId":18},
{"term":"dead","probability":0.03833642117149438,"topicId":19},
{"term":"like","probability":0.017873711992106994,"topicId":19},
{"term":"hand","probability":0.015280854355409379,"topicId":19},
{"term":"white","probability":0.013718491413582671,"topicId":19},
{"term":"blood","probability":0.012699265888344448,"topicId":19}

We are now ready to use D3 on the rawJson data.

Step 1. Downloading and Loading Data into DBFS

Here are the steps taken for downloading and saving data to the distributed file system. Uncomment them for repeating this process on your databricks cluster or for downloading a new source of data.

Unfortunately, the original data at:

is not suited for manipulation and loading into dbfs easily. So the data has been downloaded, directory renamed without white spaces, superfluous OS-specific files removed, dos2unix'd, tar -zcvf'd and uploaded to the following URL for an easily dbfs-loadable download:

wget http://lamastex.org/datasets/public/nlp/cornell_movie_dialogs_corpus.tgz
--2019-05-17 15:34:09--  http://lamastex.org/datasets/public/nlp/cornell_movie_dialogs_corpus.tgz
Resolving lamastex.org (lamastex.org)... 166.62.28.100
Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9914415 (9.5M) [application/x-tar]
Saving to: ‘cornell_movie_dialogs_corpus.tgz’

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  9550K .......... .......... .......... .......... .......... 99% 40.1M 0s
  9600K .......... .......... .......... .......... .......... 99% 45.3M 0s
  9650K .......... .......... .......... ..                   100% 33.8M=2.1s

2019-05-17 15:34:11 (4.61 MB/s) - ‘cornell_movie_dialogs_corpus.tgz’ saved [9914415/9914415]

Untar the file.

tar zxvf cornell_movie_dialogs_corpus.tgz
cornell_movie_dialogs_corpus/
cornell_movie_dialogs_corpus/movie_lines.txt
cornell_movie_dialogs_corpus/movie_characters_metadata.txt
cornell_movie_dialogs_corpus/README.txt
cornell_movie_dialogs_corpus/raw_script_urls.txt
cornell_movie_dialogs_corpus/movie_titles_metadata.txt
cornell_movie_dialogs_corpus/movie_conversations.txt
cornell_movie_dialogs_corpus/chameleons.pdf

Let us list and load all the files into dbfs after dbfs.fs.mkdirs(...) to create the directory dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/.

pwd && ls -al cornell_movie_dialogs_corpus
/databricks/driver
total 41552
drwxr-xr-x 2 ubuntu ubuntu     4096 Jan 11  2017 .
drwxr-xr-x 1 root   root       4096 May 17 15:34 ..
-rw-r--r-- 1 ubuntu ubuntu   290691 May  9  2011 chameleons.pdf
-rw-r--r-- 1 ubuntu ubuntu   705695 Jan 11  2017 movie_characters_metadata.txt
-rw-r--r-- 1 ubuntu ubuntu  6760930 Jan 11  2017 movie_conversations.txt
-rw-r--r-- 1 ubuntu ubuntu 34641919 Jan 11  2017 movie_lines.txt
-rw-r--r-- 1 ubuntu ubuntu    67289 Jan 11  2017 movie_titles_metadata.txt
-rw-r--r-- 1 ubuntu ubuntu    56177 Jan 11  2017 raw_script_urls.txt
-rw-r--r-- 1 ubuntu ubuntu     4181 Jan 11  2017 README.txt
dbutils.fs.rm("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/",true)
res4: Boolean = true
dbutils.fs.mkdirs("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/")
res5: Boolean = true
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_characters_metadata.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_conversations.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_lines.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_titles_metadata.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/raw_script_urls.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/README.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt")
res6: Boolean = true
display(dbutils.fs.ls("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/"))
path name size
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt README.txt 4181.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt movie_characters_metadata.txt 705695.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt movie_conversations.txt 6760930.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt movie_lines.txt 3.4641919e7
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt movie_titles_metadata.txt 67289.0
dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt raw_script_urls.txt 56177.0

ScaDaMaLe Course site and book

Movie Recommender using Alternating Least Squares

Most of the content below is just a scarification of from Module 5 of Anthony Joseph's edX course CS100-1x from the Community Edition of databricks.

SOURCE: Module 5 of AJ's course.

Collaborative Filtering

(watch now 61 seconds - from 18 to 79 seconds):

AJ's What is Collaborative Filtering

Let us use MLlib to make personalized movie recommendations.

We are going to use a technique called collaborative filtering. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue x than to have the opinion on x of a person chosen randomly. You can read more about collaborative filtering here.

The image below (from Wikipedia) shows an example of predicting of the user's rating using collaborative filtering. At first, people rate different items (like videos, images, games). After that, the system is making predictions about a user's rating for an item, which the user has not rated yet. These predictions are built upon the existing ratings of other users, who have similar ratings with the active user. For instance, in the image below the system has made a prediction, that the active user will not like the video.

collaborative filtering

Resources:

For movie recommendations, we start with a matrix whose entries are movie ratings by users (shown in red in the diagram below). Each row represents a user and each column represents a particular movie. Thus the entry \(r_{ij}\) represents the rating of user \(i\) for movie \(j\).

Since not all users have rated all movies, we do not know all of the entries in this matrix, which is precisely why we need collaborative filtering. For each user, we have ratings for only a subset of the movies. With collaborative filtering, the idea is to approximate the ratings matrix by factorizing it as the product of two matrices: one that describes properties of each user (shown in green), and one that describes properties of each movie (shown in blue).

factorization

We want to select these two matrices such that the error for the users/movie pairs where we know the correct ratings is minimized. The Alternating Least Squares algorithm expands on the least squares method by:

  1. first randomly filling the users matrix with values and then
  2. optimizing the value of the movies such that the error is minimized.
  3. Then, it holds the movies matrix constant and optimizes the value of the users matrix.

This alternation between which matrix to optimize is the reason for the "alternating" in the name.

This optimization is what's being shown on the right in the image above. Given a fixed set of user factors (i.e., values in the users matrix), we use the known ratings to find the best values for the movie factors using the optimization written at the bottom of the figure. Then we "alternate" and pick the best user factors given fixed movie factors.

For a simple example of what the users and movies matrices might look like, check out the videos from Lecture 8 or the slides from Lecture 8 of AJ's Introduction to Data Science course.

display(dbutils.fs.ls("/databricks-datasets/cs100/lab4/data-001/")) // The data is already here
path name size
dbfs:/databricks-datasets/cs100/lab4/data-001/movies.dat movies.dat 171308.0
dbfs:/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz ratings.dat.gz 2837683.0

Import

Let us import the relevant libraries for mllib.

import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating

Preliminaries

We read in each of the files and create an RDD consisting of parsed lines. Each line in the ratings dataset (ratings.dat.gz) is formatted as: UserID::MovieID::Rating::Timestamp Each line in the movies (movies.dat) dataset is formatted as: MovieID::Title::Genres The Genres field has the format Genres1|Genres2|Genres3|... The format of these files is uniform and simple, so we can use split().

Parsing the two files yields two RDDs

  • For each line in the ratings dataset, we create a tuple of (UserID, MovieID, Rating). We drop the timestamp because we do not need it for this exercise.
  • For each line in the movies dataset, we create a tuple of (MovieID, Title). We drop the Genres because we do not need them for this exercise.
// take a peek at what's in the rating file
sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line => line.split("::") }.take(5)
res1: Array[Array[String]] = Array(Array(1, 1193, 5, 978300760), Array(1, 661, 3, 978302109), Array(1, 914, 3, 978301968), Array(1, 3408, 4, 978300275), Array(1, 2355, 5, 978824291))
val timedRatingsRDD = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
      val fields = line.split("::")
      // format: (timestamp % 10, Rating(userId, movieId, rating))
      (fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))
    }
timedRatingsRDD: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.recommendation.Rating)] = MapPartitionsRDD[47324] at map at command-2972105651607143:1

We have a look at the first 10 entries in the Ratings RDD to check it's ok

timedRatingsRDD.take(10).map(println)
(0,Rating(1,1193,5.0))
(9,Rating(1,661,3.0))
(8,Rating(1,914,3.0))
(5,Rating(1,3408,4.0))
(1,Rating(1,2355,5.0))
(8,Rating(1,1197,3.0))
(9,Rating(1,1287,5.0))
(9,Rating(1,2804,5.0))
(8,Rating(1,594,4.0))
(8,Rating(1,919,4.0))
res2: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())

The timestamp is unused here so we want to remove it

val ratingsRDD = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
      val fields = line.split("::")
      // format: Rating(userId, movieId, rating)
      Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
    }
ratingsRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47327] at map at command-2972105651607147:1

Now our final ratings RDD looks as follows:

ratingsRDD.take(10).map(println)
Rating(1,1193,5.0)
Rating(1,661,3.0)
Rating(1,914,3.0)
Rating(1,3408,4.0)
Rating(1,2355,5.0)
Rating(1,1197,3.0)
Rating(1,1287,5.0)
Rating(1,2804,5.0)
Rating(1,594,4.0)
Rating(1,919,4.0)
res3: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())

A similar command is used to format the movies. We ignore the genres in this recommender

val movies = sc.textFile("/databricks-datasets/cs100/lab4/data-001/movies.dat").map { line =>
      val fields = line.split("::")
      // format: (movieId, movieName)
      (fields(0).toInt, fields(1))
    }.collect.toMap
movies: scala.collection.immutable.Map[Int,String] = Map(2163 -> Attack of the Killer Tomatoes! (1980), 645 -> Nelly & Monsieur Arnaud (1995), 892 -> Twelfth Night (1996), 69 -> Friday (1995), 2199 -> Phoenix (1998), 3021 -> Funhouse, The (1981), 1322 -> Amityville 1992: It's About Time (1992), 1665 -> Bean (1997), 1036 -> Die Hard (1988), 2822 -> Medicine Man (1992), 2630 -> Besieged (L' Assedio) (1998), 3873 -> Cat Ballou (1965), 1586 -> G.I. Jane (1997), 1501 -> Keys to Tulsa (1997), 2452 -> Gate II: Trespassers, The (1990), 809 -> Fled (1996), 1879 -> Hanging Garden, The (1997), 1337 -> Body Snatcher, The (1945), 1718 -> Stranger in the House (1997), 2094 -> Rocketeer, The (1991), 3944 -> Bootmen (2000), 1411 -> Hamlet (1996), 629 -> Rude (1995), 3883 -> Catfish in Black Bean Sauce (2000), 2612 -> Mildred Pierce (1945), 1024 -> Three Caballeros, The (1945), 365 -> Little Buddha (1993), 2744 -> Otello (1986), 1369 -> I Can't Sleep (J'ai pas sommeil) (1994), 138 -> Neon Bible, The (1995), 2889 -> Mystery, Alaska (1999), 1190 -> Tie Me Up! Tie Me Down! (1990), 1168 -> Bad Moon (1996), 2295 -> Impostors, The (1998), 2306 -> Holy Man (1998), 3053 -> Messenger: The Story of Joan of Arc, The (1999), 3345 -> Charlie, the Lonesome Cougar (1967), 760 -> Stalingrad (1993), 2341 -> Dancing at Lughnasa (1998), 101 -> Bottle Rocket (1996), 2336 -> Elizabeth (1998), 3008 -> Last Night (1998), 2109 -> Jerk, The (1979), 2131 -> Autumn Sonata (H�stsonaten ) (1978), 1454 -> SubUrbia (1997), 2031 -> $1,000,000 Duck (1971), 1633 -> Ulee's Gold (1997), 2778 -> Never Talk to Strangers (1995), 2072 -> 'burbs, The (1989), 3661 -> Puppet Master II (1990), 1767 -> Music From Another Room (1998), 3399 -> Sesame Street Presents Follow That Bird (1985), 1995 -> Poltergeist II: The Other Side (1986), 2263 -> Seventh Sign, The (1988), 3930 -> Creature From the Black Lagoon, The (1954), 479 -> Judgment Night (1993), 3798 -> What Lies Beneath (2000), 1559 -> Next Step, The (1995), 1105 -> Children of the Corn IV: The Gathering (1996), 347 -> Bitter Moon (1992), 3666 -> Retro Puppetmaster (1999), 1729 -> Jackie Brown (1997), 3434 -> Death Wish V: The Face of Death (1994), 3167 -> Carnal Knowledge (1971), 2412 -> Rocky V (1990), 2876 -> Thumbelina (1994), 1237 -> Seventh Seal, The (Sjunde inseglet, Det) (1957), 846 -> Flirt (1995), 909 -> Apartment, The (1960), 2921 -> High Plains Drifter (1972), 3477 -> Empire Records (1995), 3698 -> Running Man, The (1987), 333 -> Tommy Boy (1995), 628 -> Primal Fear (1996), 1031 -> Bedknobs and Broomsticks (1971), 249 -> Immortal Beloved (1994), 2463 -> Ruthless People (1986), 3397 -> Great Muppet Caper, The (1981), 1899 -> Passion in the Desert (1998), 893 -> Mother Night (1996), 1840 -> He Got Game (1998), 3581 -> Human Traffic (1999), 1315 -> Paris Was a Woman (1995), 3863 -> Cell, The (2000), 3830 -> Psycho Beach Party (2000), 2787 -> Cat's Eye (1985), 2595 -> Photographer (Fotoamator) (1998), 518 -> Road to Wellville, The (1994), 1850 -> I Love You, Don't Touch Me! (1998), 2499 -> God Said 'Ha!' (1998), 2427 -> Thin Red Line, The (1998), 2480 -> Dry Cleaning (Nettoyage � sec) (1997), 1083 -> Great Race, The (1965), 962 -> They Made Me a Criminal (1939), 1982 -> Halloween (1978), 468 -> Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995), 2559 -> King and I, The (1999), 3449 -> Good Mother, The (1988), 234 -> Exit to Eden (1994), 2544 -> School of Flesh, The (L' �cole de la chair) (1998), 941 -> Mark of Zorro, The (1940), 3927 -> Fantastic Voyage (1966), 1179 -> Grifters, The (1990), 2331 -> Living Out Loud (1998), 777 -> Pharaoh's Army (1995), 3566 -> Big Kahuna, The (2000), 555 -> True Romance (1993), 666 -> All Things Fair (1996), 1295 -> Unbearable Lightness of Being, The (1988), 1956 -> Ordinary People (1980), 1950 -> In the Heat of the Night (1967), 88 -> Black Sheep (1996), 1549 -> Rough Magic (1995), 2280 -> Clay Pigeons (1998), 1554 -> Pillow Book, The (1995), 1110 -> Bird of Prey (1996), 3172 -> Ulysses (Ulisse) (1954), 1686 -> Red Corner (1997), 481 -> Kalifornia (1993), 352 -> Crooklyn (1994), 2250 -> Men Don't Leave (1990), 2363 -> Godzilla (Gojira) (1954), 1855 -> Krippendorf's Tribe (1998), 3680 -> Decline of Western Civilization Part II: The Metal Years, The (1988), 1200 -> Aliens (1986), 2077 -> Journey of Natty Gann, The (1985), 3534 -> 28 Days (2000), 1750 -> Star Kid (1997), 3185 -> Snow Falling on Cedars (1999), 408 -> 8 Seconds (1994), 977 -> Moonlight Murder (1936), 170 -> Hackers (1995), 3681 -> For a Few Dollars More (1965), 1211 -> Wings of Desire (Der Himmel �ber Berlin) (1987), 523 -> Ruby in Paradise (1993), 1158 -> Here Comes Cookie (1935), 2309 -> Inheritors, The (Die Siebtelbauern) (1998), 2512 -> Ballad of Narayama, The (Narayama Bushiko) (1982), 582 -> Metisse (Caf� au Lait) (1993), 2976 -> Bringing Out the Dead (1999), 762 -> Striptease (1996), 3072 -> Moonstruck (1987), 1924 -> Plan 9 from Outer Space (1958), 1005 -> D3: The Mighty Ducks (1996), 2210 -> Sabotage (1936), 2117 -> Nineteen Eighty-Four (1984), 2940 -> Gilda (1946), 1596 -> Career Girls (1997), 1406 -> C�r�monie, La (1995), 115 -> Happiness Is in the Field (1995), 2104 -> Tex (1982), 3317 -> Wonder Boys (2000), 683 -> Eye of Vichy, The (Oeil de Vichy, L') (1993), 730 -> Low Life, The (1994), 1290 -> Some Kind of Wonderful (1987), 1882 -> Godzilla (1998), 217 -> Babysitter, The (1995), 276 -> Milk Money (1994), 2231 -> Rounders (1998), 2622 -> Midsummer Night's Dream, A (1999), 1068 -> Crossfire (1947), 3858 -> Cecil B. Demented (2000), 2808 -> Universal Soldier (1992), 3905 -> Specials, The (2000), 1522 -> Ripe (1996), 3762 -> Daughter of Dr. Jeckyll (1957), 2644 -> Dracula (1931), 3813 -> Interiors (1978), 2495 -> Fantastic Planet, The (La Plan�te sauvage) (1973), 3230 -> Odessa File, The (1974), 2381 -> Police Academy 4: Citizens on Patrol (1987), 2395 -> Rushmore (1998), 2908 -> Boys Don't Cry (1999), 2062 -> Governess, The (1998), 3549 -> Guys and Dolls (1955), 1443 -> Tickle in the Heart, A (1996), 2776 -> Marcello Mastroianni: I Remember Yes, I Remember (1997), 2659 -> It Came from Hollywood (1982), 3417 -> Crimson Pirate, The (1952), 3509 -> Black and White (1999), 3285 -> Beach, The (2000), 3377 -> Hangmen Also Die (1943), 994 -> Big Night (1996), 2527 -> Westworld (1973), 3040 -> Meatballs (1979), 3153 -> 7th Voyage of Sinbad, The (1958), 2953 -> Home Alone 2: Lost in New York (1992), 2590 -> Hideous Kinky (1998), 1401 -> Ghosts of Mississippi (1996), 3460 -> Hillbillys in a Haunted House (1967), 1422 -> Murder at 1600 (1997), 308 -> Three Colors: White (1994), 2947 -> Goldfinger (1964), 1569 -> My Best Friend's Wedding (1997), 1939 -> Best Years of Our Lives, The (1946), 2248 -> Say Anything... (1989), 3912 -> Beautiful (2000), 3098 -> Natural, The (1984), 741 -> Ghost in the Shell (Kokaku kidotai) (1995), 1073 -> Willy Wonka and the Chocolate Factory (1971), 2671 -> Notting Hill (1999), 1544 -> Lost World: Jurassic Park, The (1997), 2676 -> Instinct (1999), 2014 -> Freaky Friday (1977), 3910 -> Dancer in the Dark (2000), 5 -> Father of the Bride Part II (1995), 1728 -> Winter Guest, The (1997), 3108 -> Fisher King, The (1991), 873 -> Shadow of Angels (Schatten der Engel) (1976), 3012 -> Battling Butler (1926), 1205 -> Transformers: The Movie, The (1986), 449 -> Fear of a Black Hat (1993), 120 -> Race the Sun (1996), 2099 -> Song of the South (1946), 2282 -> Pecker (1998), 247 -> Heavenly Creatures (1994), 1591 -> Spawn (1997), 2114 -> Outsiders, The (1983), 2837 -> Bedrooms & Hallways (1998), 1142 -> Get Over It (1996), 379 -> Timecop (1994), 1269 -> Arsenic and Old Lace (1944), 878 -> Bye-Bye (1995), 440 -> Dave (1993), 655 -> Mutters Courage (1995), 3498 -> Midnight Express (1978), 511 -> Program, The (1993), 2380 -> Police Academy 3: Back in Training (1986), 1971 -> Nightmare on Elm Street 4: The Dream Master, A (1988), 1793 -> Welcome to Woop-Woop (1997), 3402 -> Turtle Diary (1985), 2854 -> Don't Look in the Basement! (1973), 1533 -> Promise, The (La Promesse) (1996), 614 -> Loaded (1994), 1692 -> Alien Escape (1995), 269 -> My Crazy Life (Mi vida loca) (1993), 1305 -> Paris, Texas (1984), 202 -> Total Eclipse (1995), 597 -> Pretty Woman (1990), 1437 -> Cement Garden, The (1993), 1041 -> Secrets & Lies (1996), 861 -> Supercop (1992), 3382 -> Song of Freedom (1936), 1173 -> Cook the Thief His Wife & Her Lover, The (1989), 1486 -> Quiet Room, The (1996), 3848 -> Silent Fall (1994), 1497 -> Double Team (1997), 10 -> GoldenEye (1995), 2195 -> Dirty Work (1998), 1705 -> Guy (1996), 3017 -> Creepshow 2 (1987), 1078 -> Bananas (1971), 1788 -> Men With Guns (1997), 1426 -> Zeus and Roxanne (1997), 3217 -> Star Is Born, A (1937), 3530 -> Smoking/No Smoking (1993), 1671 -> Deceiver (1997), 3794 -> Chuck & Buck (2000), 1608 -> Air Force One (1997), 3439 -> Teenage Mutant Ninja Turtles II: The Secret of the Ooze (1991), 385 -> Man of No Importance, A (1994), 384 -> Bad Company (1995), 56 -> Kids of the Round Table (1995), 1655 -> Phantoms (1998), 3120 -> Distinguished Gentleman, The (1992), 3429 -> Creature Comforts (1990), 3049 -> How I Won the War (1967), 3745 -> Titan A.E. (2000), 1137 -> Hustler White (1996), 2607 -> Get Real (1998), 2686 -> Red Violin, The (Le Violon rouge) (1998), 1756 -> Prophecy II, The (1998), 1310 -> Hype! (1996), 533 -> Shadow, The (1994), 3004 -> Bachelor, The (1999), 2035 -> Blackbeard's Ghost (1968), 3332 -> Legend of Lobo, The (1962), 3367 -> Devil's Brigade, The (1968), 3648 -> Abominable Snowman, The (1957), 3135 -> Great Santini, The (1979), 3942 -> Sorority House Massacre II (1990), 550 -> Threesome (1994), 3649 -> American Gigolo (1980), 3365 -> Searchers, The (1956), 142 -> Shadows (Cienie) (1988), 2740 -> Kindred, The (1986), 2918 -> Ferris Bueller's Day Off (1986), 3044 -> Dead Again (1991), 1735 -> Great Expectations (1998), 1867 -> Tarzan and the Lost City (1998), 500 -> Mrs. Doubtfire (1993), 2184 -> Trouble with Harry, The (1955), 3175 -> Galaxy Quest (1999), 1164 -> Two or Three Things I Know About Her (1966), 1999 -> Exorcist III, The (1990), 797 -> Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991), 2316 -> Practical Magic (1998), 3446 -> Funny Bones (1995), 715 -> Horseman on the Roof, The (Hussard sur le toit, Le) (1995), 2448 -> Virus (1999), 3338 -> For All Mankind (1989), 2933 -> Fire Within, The (Le Feu Follet) (1963), 1275 -> Highlander (1986), 2141 -> American Tail, An (1986), 2434 -> Down in the Delta (1998), 1872 -> Go Now (1995), 2712 -> Eyes Wide Shut (1999), 472 -> I'll Do Anything (1994), 3616 -> Loser (2000), 1233 -> Boat, The (Das Boot) (1981), 3781 -> Shaft in Africa (1973), 814 -> Boy Called Hate, A (1995), 1327 -> Amityville Horror, The (1979), 3693 -> Toxic Avenger, The (1985), 2476 -> Heartbreak Ridge (1986), 2627 -> Endurance (1998), 2168 -> Dance with Me (1998), 1260 -> M (1931), 698 -> Delta of Venus (1994), 1919 -> Madeline (1998), 3253 -> Wayne's World (1992), 1988 -> Hello Mary Lou: Prom Night II (1987), 3826 -> Hollow Man (2000), 2459 -> Texas Chainsaw Massacre, The (1974), 3414 -> Love Is a Many-Splendored Thing (1955), 1342 -> Candyman (1992), 3121 -> Hitch-Hiker, The (1953), 747 -> Stupids, The (1996), 913 -> Maltese Falcon, The (1941), 3481 -> High Fidelity (2000), 3562 -> Committed (2000), 1640 -> How to Be a Player (1997), 2580 -> Go (1999), 2901 -> Phantasm (1979), 945 -> Top Hat (1935), 3313 -> Class Reunion (1982), 1063 -> Johns (1996), 1954 -> Rocky (1976), 2844 -> Minus Man, The (1999), 340 -> War, The (1994), 3577 -> Two Moon Juction (1988), 2042 -> D2: The Mighty Ducks (1994), 538 -> Six Degrees of Separation (1993), 1354 -> Breaking the Waves (1996), 153 -> Batman Forever (1995), 2146 -> St. Elmo's Fire (1985), 1507 -> Paradise Road (1997), 1222 -> Full Metal Jacket (1987), 930 -> Notorious (1946), 3895 -> Watcher, The (2000), 3717 -> Gone in 60 Seconds (2000), 2360 -> Celebration, The (Festen) (1998), 1458 -> Touch (1997), 670 -> World of Apu, The (Apur Sansar) (1959), 3276 -> Gun Shy (2000), 2575 -> Dreamlife of Angels, The (La Vie r�v�e des anges) (1998), 829 -> Joe's Apartment (1996), 3370 -> Betrayed (1988), 174 -> Jury Duty (1995), 1095 -> Glengarry Glen Ross (1992), 404 -> Brother Minister: The Assassination of Malcolm X (1994), 3335 -> Jail Bait (1954), 1196 -> Star Wars: Episode V - The Empire Strikes Back (1980), 1746 -> Senseless (1998), 3103 -> Stanley & Iris (1990), 898 -> Philadelphia Story, The (1940), 185 -> Net, The (1995), 1835 -> City of Angels (1998), 3836 -> Kelly's Heroes (1970), 2216 -> Skin Game, The (1931), 3236 -> Zachariah (1971), 3629 -> Gold Rush, The (1925), 2348 -> Sid and Nancy (1986), 3350 -> Raisin in the Sun, A (1961), 1001 -> Associate, The (L'Associe)(1982), 3545 -> Cabaret (1972), 2723 -> Mystery Men (1999), 2972 -> Red Sorghum (Hong Gao Liang) (1987), 3634 -> Seven Days in May (1964), 2046 -> Flight of the Navigator (1986), 3766 -> Missing in Action (1984), 2432 -> Stepmom (1998), 1914 -> Smoke Signals (1998), 2491 -> Simply Irresistible (1999), 1243 -> Rosencrantz and Guildenstern Are Dead (1990), 1127 -> Abyss, The (1989), 2763 -> Thomas Crown Affair, The (1999), 3915 -> Girlfight (2000), 2548 -> Rage: Carrie 2, The (1999), 1782 -> Little City (1998), 3502 -> My Life (1993), 42 -> Dead Presidents (1995), 2985 -> Robocop (1987), 2227 -> Lodger, The (1926), 1391 -> Mars Attacks! (1996), 3249 -> Hand That Rocks the Cradle, The (1992), 782 -> Fan, The (1996), 1441 -> Benny & Joon (1993), 709 -> Oliver & Company (1988), 2020 -> Dangerous Liaisons (1988), 841 -> Eyes Without a Face (1959), 3513 -> Rules of Engagement (2000), 417 -> Barcelona (1994), 24 -> Powder (1995), 973 -> Meet John Doe (1941), 885 -> Bogus (1996), 3088 -> Harvey (1950), 3777 -> Nekromantik (1987), 1046 -> Beautiful Thing (1996), 288 -> Natural Born Killers (1994), 1613 -> Star Maps (1997), 3308 -> Flamingo Kid, The (1984), 2010 -> Metropolis (1926), 1935 -> How Green Was My Valley (1941), 3947 -> Get Carter (1971), 1650 -> Washington Square (1997), 3730 -> Conversation, The (1974), 1645 -> Devil's Advocate, The (1997), 1921 -> Pi (1998), 2886 -> Adventures of Elmo in Grouchland, The (1999), 1359 -> Jingle All the Way (1996), 1601 -> Hoodlum (1997), 1386 -> Terror in a Texas Town (1958), 3281 -> Brandon Teena Story, The (1998), 3466 -> Heart and Souls (1993), 301 -> Picture Bride (1995), 3898 -> Bait (2000), 2082 -> Mighty Ducks, The (1992), 3140 -> Three Ages, The (1923), 1724 -> Full Speed (1996), 1475 -> Kama Sutra: A Tale of Love (1996), 320 -> Suture (1993), 3809 -> What About Bob? (1991), 2173 -> Navigator: A Mediaeval Odyssey, The (1988), 565 -> Cronos (1992), 1366 -> Crucible, The (1996), 2516 -> Children of the Corn III (1994), 2400 -> Prancer (1989), 2067 -> Doctor Zhivago (1965), 2825 -> Rosie (1998), 1529 -> Nowhere (1997), 1967 -> Labyrinth (1986), 2819 -> Three Days of the Condor (1975), 436 -> Color of Night (1994), 3880 -> Ballad of Ramblin' Jack, The (2000), 3878 -> X: The Unknown (1956), 2136 -> Nutty Professor, The (1963), 37 -> Across the Sea of Time (1995), 1904 -> Henry Fool (1997), 1518 -> Breakdown (1997), 1265 -> Groundhog Day (1993), 1703 -> For Richer or Poorer (1997), 2531 -> Battle for the Planet of the Apes (1973), 2405 -> Jewel of the Nile, The (1985), 1228 -> Raging Bull (1980), 1623 -> Wishmaster (1997), 1482 -> Van, The (1996), 3597 -> Whipped (2000), 25 -> Leaving Las Vegas (1995), 1254 -> Treasure of the Sierra Madre, The (1948), 3688 -> Porky's (1981), 2869 -> Separation, The (La S�paration) (1994), 1887 -> Almost Heroes (1998), 651 -> Superweib, Das (1996), 2957 -> Sparrows (1926), 257 -> Just Cause (1995), 3820 -> Thomas and the Magic Railroad (2000), 3891 -> Turn It Up (2000), 2654 -> Wolf Man, The (1941), 389 -> Colonel Chabert, Le (1994), 1628 -> Locusts, The (1997), 3248 -> Sister Act 2: Back in the Habit (1993), 52 -> Mighty Aphrodite (1995), 1055 -> Shadow Conspiracy (1997), 1409 -> Michael (1996), 724 -> Craft, The (1996), 3204 -> Boys from Brazil, The (1978), 3442 -> Band of the Hand (1986), 3263 -> White Men Can't Jump (1992), 1287 -> Ben-Hur (1959), 3221 -> Draughtsman's Contract, The (1982), 14 -> Nixon (1995), 1709 -> Legal Deceit (1997), 2603 -> N� (1998), 2328 -> Vampires (1998), 2916 -> Total Recall (1990), 3938 -> Slumber Party Massacre, The (1982), 3195 -> Tess of the Storm Country (1922), 3029 -> Nighthawks (1981), 570 -> Slingshot, The (K�disbellan ) (1993), 3425 -> Mo' Better Blues (1990), 1792 -> U.S. Marshalls (1998), 1430 -> Underworld (1997), 3644 -> Dark Command (1940), 2735 -> Golden Child, The (1986), 2814 -> Bat, The (1959), 1985 -> Halloween 4: The Return of Michael Myers (1988), 2039 -> Cheetah (1989), 3275 -> Boondock Saints, The (1999), 184 -> Nadja (1994), 2698 -> Zone 39 (1997), 1760 -> Spice World (1997), 3131 -> Broadway Damage (1997), 1660 -> Eve's Bayou (1997), 1298 -> Pink Floyd - The Wall (1982), 3116 -> Miss Julie (1999), 2667 -> Mole People, The (1956), 719 -> Multiplicity (1996), 2339 -> I'll Be Home For Christmas (1998), 785 -> Kingpin (1996), 3046 -> Incredibly True Adventure of Two Girls in Love, The (1995), 3689 -> Porky's II: The Next Day (1983), 3342 -> Birdy (1984), 2269 -> Indecent Proposal (1993), 2755 -> Light of Day (1987), 372 -> Reality Bites (1994), 3495 -> Roadside Prophets (1992), 504 -> No Escape (1994), 1871 -> Friend of the Deceased, A (1997), 3574 -> Carnosaur 3: Primal Species (1996), 3061 -> Holiday Inn (1942), 110 -> Braveheart (1995), 2708 -> Autumn Tale, An (Conte d'automne) (1998), 3821 -> Nutty Professor II: The Klumps (2000), 1907 -> Mulan (1998), 2501 -> October Sky (1999), 3617 -> Road Trip (2000), 1330 -> April Fool's Day (1986), 2444 -> 24 7: Twenty Four Seven (1997), 1860 -> Character (Karakter) (1997), 1264 -> Diva (1981), 2217 -> Elstree Calling (1930), 587 -> Ghost (1990), 1323 -> Amityville 3-D (1983), 3612 -> Slipper and the Rose, The (1976), 3148 -> Cider House Rules, The (1999), 3653 -> Endless Summer, The (1966), 619 -> Ed (1996), 838 -> Emma (1996), 1511 -> A Chef in Love (1996), 2274 -> Lilian's Story (1995), 917 -> Little Princess, The (1939), 702 -> Faces (1968), 751 -> Careful (1992), 3485 -> Autopsy (Macchie Solari) (1975), 802 -> Phenomenon (1996), 125 -> Flirting With Disaster (1996), 344 -> Ace Ventura: Pet Detective (1994), 3776 -> Melody Time (1948), 2682 -> Limbo (1999), 3125 -> End of the Affair, The (1999), 1826 -> Barney's Great Adventure (1998), 3542 -> Coming Apart (1969), 1313 -> Mad Dog Time (1996), 1279 -> Night on Earth (1991), 3410 -> Soft Fruit (1999), 2185 -> I Confess (1953), 1577 -> Mondo (1996), 1455 -> Hotel de Love (1996), 3280 -> Baby, The (1973), 3749 -> Time Regained (Le Temps Retrouv�) (1999), 3845 -> And God Created Woman (Et Dieu&#8230;Cr�a la Femme) (1956), 2562 -> Bandits (1997), 3089 -> Bicycle Thief, The (Ladri di biciclette) (1948), 2488 -> Peeping Tom (1960), 1832 -> Heaven's Burning (1997), 2897 -> And the Ship Sails On (E la nave va) (1984), 934 -> Father of the Bride (1950), 357 -> Four Weddings and a Funeral (1994), 1191 -> Madonna: Truth or Dare (1991), 3744 -> Shaft (2000), 1992 -> Child's Play 2 (1990), 3353 -> Closer You Get, The (2000), 3803 -> Greaser's Palace (1972), 2466 -> Belizaire the Cajun (1986), 2126 -> Snake Eyes (1998), 196 -> Species (1995), 1462 -> Unforgotten: Twenty-Five Years After Willowbrook (1996), 1059 -> William Shakespeare's Romeo and Juliet (1996), 1132 -> Manon of the Spring (Manon des sources) (1986), 3043 -> Meatballs 4 (1992), 3855 -> Affair of Love, An (Une Liaison Pornographique) (1999), 949 -> East of Eden (1955), 2745 -> Mission, The (1986), 2571 -> Matrix, The (1999), 3713 -> Long Walk Home, The (1990), 2356 -> Celebrity (1998), 542 -> Son in Law (1993), 460 -> Getting Even with Dad (1994), 157 -> Canadian Bacon (1994), 2703 -> Broken Vessels (1998), 1545 -> Ponette (1996), 2420 -> Karate Kid, The (1984), 1922 -> Whatever (1998), 2691 -> Legend of 1900, The (Leggenda del pianista sull'oceano) (1998), 902 -> Breakfast at Tiffany's (1961), 559 -> Paris, France (1993), 3099 -> Shampoo (1975), 2713 -> Lake Placid (1999), 3517 -> Bells, The (1926), 638 -> Jack and Sarah (1995), 853 -> Dingo (1992), 3840 -> Pumpkinhead (1988), 1892 -> Perfect Murder, A (1998), 3463 -> Last Resort (1994), 1379 -> Young Guns II (1990), 3374 -> Daughters of the Dust (1992), 3385 -> Volunteers (1985), 2857 -> Yellow Submarine (1968), 2169 -> Dead Man on Campus (1998), 2403 -> First Blood (1982), 3638 -> Moonraker (1979), 1087 -> Madame Butterfly (1995), 1514 -> Temptress Moon (Feng Yue) (1996), 189 -> Reckless (1995), 20 -> Money Train (1995), 1147 -> When We Were Kings (1996), 1247 -> Graduate, The (1967), 2049 -> Happiest Millionaire, The (1967), 2552 -> My Boyfriend's Back (1993), 1319 -> Kids of Survival (1993), 1704 -> Good Will Hunting (1997), 421 -> Black Beauty (1994), 870 -> Gone Fishin' (1997), 1890 -> Little Boy Blue (1997), 2767 -> Illuminata (1998), 1479 -> Saint, The (1997), 46 -> How to Make an American Quilt (1995), 1609 -> 187 (1997), 969 -> African Queen, The (1951), 93 -> Vampire in Brooklyn (1995), 2373 -> Red Sonja (1985), 606 -> Candyman: Farewell to the Flesh (1995), 1347 -> Nightmare on Elm Street, A (1984), 1572 -> Contempt (Le M�pris) (1963), 3919 -> Hellraiser III: Hell on Earth (1992), 1013 -> Parent Trap, The (1961), 3216 -> Vampyros Lesbos (Las Vampiras) (1970), 284 -> New York Cop (1996), 770 -> Costa Brava (1946), 1741 -> Midaq Alley (Callej�n de los milagros, El) (1995), 1398 -> In Love and War (1996), 881 -> First Kid (1996), 3084 -> Home Page (1999), 416 -> Bad Girls (1994), 1115 -> Sleepover (1995), 2635 -> Mummy's Curse, The (1944), 2929 -> Reds (1981), 325 -> National Lampoon's Senior Trip (1995), 3470 -> Dersu Uzala (1974), 3312 -> McCullochs, The (1975), 3902 -> Goya in Bordeaux (Goya en Bodeos) (1999), 3657 -> Pandora and the Flying Dutchman (1951), 1931 -> Mutiny on the Bounty (1935), 152 -> Addiction, The (1995), 1568 -> MURDER and murder (1996), 3734 -> Prince of the City (1981), 3014 -> Bustin' Loose (1981), 2520 -> Airport (1970), 2730 -> Barry Lyndon (1975), 228 -> Destiny Turns on the Radio (1995), 3951 -> Two Family House (2000), 2158 -> Henry: Portrait of a Serial Killer, Part 2 (1996), 2024 -> Rapture, The (1991), 1641 -> Full Monty, The (1997), 3835 -> Crush, The (1993), 3708 -> Firestarter (1984), 289 -> Only You (1994), 1773 -> Tokyo Fist (1995), 448 -> Fearless (1993), 2301 -> History of the World: Part I (1981), 1815 -> Eden (1997), 1494 -> Sixth Man, The (1997), 57 -> Home for the Holidays (1995), 2989 -> For Your Eyes Only (1981), 316 -> Stargate (1994), 1362 -> Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970), 1963 -> Take the Money and Run (1969), 3180 -> Play it to the Bone (1999), 3602 -> G. I. Blues (1960), 3557 -> Jennifer 8 (1992), 78 -> Crossing Guard, The (1995), 1875 -> Clockwatchers (1997), 2852 -> Soldier's Story, A (1984), 3231 -> Saphead, The (1920), 2388 -> Steam: The Turkish Bath (Hamam) (1997), 3866 -> Sunset Strip (2000), 2533 -> Escape from the Planet of the Apes (1971), 1100 -> Days of Thunder (1990), 261 -> Little Women (1994), 1232 -> Stalker (1979), 3268 -> Stop! Or My Mom Will Shoot (1992), 3258 -> Death Becomes Her (1992), 1847 -> Ratchet (1996), 29 -> City of Lost Children, The (1995), 2829 -> Muse, The (1999), 3788 -> Blowup (1966), 2456 -> Fly II, The (1989), 2007 -> Polish Wedding (1998), 2178 -> Frenzy (1972), 2222 -> Champagne (1928), 216 -> Billy Madison (1995), 1489 -> Cats Don't Dance (1997), 2950 -> Blue Lagoon, The (1980), 2237 -> Without Limits (1998), 1415 -> Thieves (Voleurs, Les) (1996), 475 -> In the Name of the Father (1993), 924 -> 2001: A Space Odyssey (1968), 3322 -> 3 Strikes (2000), 2616 -> Dick Tracy (1990), 1946 -> Marty (1955), 492 -> Manhattan Murder Mystery (1993), 164 -> Devil in a Blue Dress (1995), 1465 -> Rosewood (1997), 3524 -> Arthur (1981), 3431 -> Death Wish II (1982), 1302 -> Field of Dreams (1989), 756 -> Carmen Miranda: Bananas Is My Business (1994), 1664 -> N�nette et Boni (1996), 2794 -> European Vacation (1985), 3887 -> Went to Coney Island on a Mission From God... Be Back by Five (1998), 2324 -> Life Is Beautiful (La Vita � bella) (1997), 1714 -> Never Met Picasso (1996), 2027 -> Mafia! (1998), 3199 -> Pal Joey (1957), 789 -> I, Worst of All (Yo, la peor de todas) (1990), 3934 -> Kronos (1957), 2105 -> Tron (1982), 3075 -> Repulsion (1965), 1811 -> Niagara, Niagara (1997), 179 -> Mad Love (1995), 1450 -> Prisoner of the Mountains (Kavkazsky Plennik) (1996), 2090 -> Rescuers, The (1977), 591 -> Tough and Deadly (1995), 3393 -> Date with an Angel (1987), 1373 -> Star Trek V: The Final Frontier (1989), 2996 -> Music of the Heart (1999), 443 -> Endless Summer 2, The (1994), 3724 -> Coming Home (1978), 1911 -> Doctor Dolittle (1998), 3295 -> Raining Stones (1993), 2782 -> Pit and the Pendulum (1961), 1040 -> Secret Agent, The (1996), 1109 -> Charm's Incidents (1996), 2893 -> Plunkett & MaCleane (1999), 1632 -> Smile Like Yours, A (1997), 321 -> Strawberry and Chocolate (Fresa y chocolate) (1993), 2469 -> Peggy Sue Got Married (1986), 3670 -> Story of G.I. Joe, The (1945), 376 -> River Wild, The (1994), 2259 -> Blame It on Rio (1984), 3163 -> Topsy-Turvy (1999), 2650 -> Ghost of Frankenstein, The (1942), 3453 -> Here on Earth (2000), 2451 -> Gate, The (1987), 2236 -> Simon Birch (1998), 623 -> Modern Affair, A (1995), 1334 -> Blob, The (1958), 211 -> Browning Version, The (1994), 3527 -> Predator (1987), 3473 -> Jonah Who Will Be 25 in the Year 2000 (1976), 3702 -> Mad Max (1979), 253 -> Interview with the Vampire (1994), 2880 -> Operation Condor 2 (Longxiong hudi) (1990), 1682 -> Truman Show, The (1998), 485 -> Last Action Hero (1993), 1136 -> Monty Python and the Holy Grail (1974), 834 -> Phat Beach (1996), 1858 -> Mr. Nice Guy (1997), 3438 -> Teenage Mutant Ninja Turtles (1990), 3525 -> Bachelor Party (1984), 1864 -> Sour Grapes (1998), 2122 -> Children of the Corn (1984), 106 -> Nobody Loves Me (Keiner liebt mich) (1994), 238 -> Far From Home: The Adventures of Yellow Dog (1995), 3621 -> Possession (1981), 2925 -> Conformist, The (Il Conformista) (1970), 1978 -> Friday the 13th Part V: A New Beginning (1985), 121 -> Boys of St. Vincent, The (1993), 514 -> Ref, The (1994), 1151 -> Faust (1994), 937 -> Love in the Afternoon (1957), 2291 -> Edward Scissorhands (1990), 1383 -> Adrenalin: Fear the Rush (1996), 1020 -> Cool Runnings (1993), 348 -> Bullets Over Broadway (1994), 3357 -> East-West (Est-ouest) (1999), 574 -> Spanking the Monkey (1994), 2254 -> Choices (1981), 3569 -> Idiots, The (Idioterne) (1998), 1677 -> Critical Care (1997), 2540 -> Corruptor, The (1999), 2034 -> Black Hole, The (1979), 3923 -> Return of the Fly (1959), 1960 -> Last Emperor, The (1987), 2555 -> Baby Geniuses (1999), 3290 -> Soft Toilet Seats (1999), 806 -> American Buffalo (1996), 84 -> Last Summer in the Hamptons (1995), 3300 -> Pitch Black (2000), 3093 -> McCabe & Mrs. Miller (1971), 1581 -> Out to Sea (1997), 353 -> Crow, The (1994), 2335 -> Waterboy, The (1998), 966 -> Walk in the Sun, A (1945), 2777 -> Cobra (1925), 3152 -> Last Picture Show, The (1971), 1183 -> English Patient, The (1996), 1283 -> High Noon (1952), 2073 -> Fandango (1985), 1051 -> Trees Lounge (1996), 3771 -> Golden Voyage of Sinbad, The (1974), 821 -> Crude Oasis, The (1995), 3851 -> I'm the One That I Want (2000), 2017 -> Babes in Toyland (1961), 905 -> It Happened One Night (1934), 480 -> Jurassic Park (1993), 3389 -> Let's Get Harry (1986), 602 -> Great Day in Harlem, A (1994), 2508 -> Breaks, The (1999), 981 -> Dangerous Ground (1997), 766 -> I Shot Andy Warhol (1996), 2964 -> Julien Donkey-Boy (1999), 147 -> Basketball Diaries, The (1995), 1843 -> Slappy and the Stinkers (1998), 2058 -> Negotiator, The (1998), 1169 -> American Dream (1990), 397 -> Fear, The (1995), 687 -> Country Life (1994), 2367 -> King Kong (1976), 2437 -> Wilde (1997), 3537 -> Where the Money Is (2000), 1928 -> Cimarron (1931), 3068 -> Verdict, The (1982), 1377 -> Batman Returns (1992), 280 -> Murder in the First (1995), 2618 -> Castle, The (1997), 2833 -> Lucie Aubrac (1997), 3189 -> My Dog Skip (1999), 3739 -> Trouble in Paradise (1932), 2281 -> Monument Ave. (1998), 2861 -> For Love of the Game (1999), 1215 -> Army of Darkness (1993), 61 -> Eye for an Eye (1996), 634 -> Theodore Rex (1995), 1896 -> Cousin Bette (1998), 877 -> Girls Town (1996), 692 -> Solo (1996), 2190 -> Why Do Fools Fall In Love? (1998), 3421 -> Animal House (1978), 734 -> Getting Away With Murder (1996), 3712 -> Soapdish (1991), 1447 -> Gridlock'd (1997), 2483 -> Day of the Beast, The (El D�a de la bestia) (1995), 293 -> Professional, The (a.k.a. Leon: The Professional) (1994), 956 -> Penny Serenade (1941), 3505 -> No Way Out (1987), 3685 -> Prizzi's Honor (1985), 866 -> Bound (1996), 1886 -> I Got the Hook Up (1998), 3808 -> Two Women (La Ciociara) (1961), 2523 -> Rollercoaster (1977), 1796 -> In God's Hands (1998), 3036 -> Quest for Fire (1981), 2530 -> Beneath the Planet of the Apes (1970), 453 -> For Love or Money (1993), 1119 -> Drunks (1997), 132 -> Jade (1995), 1622 -> Kicked in the Head (1997), 2424 -> You've Got Mail (1998), 774 -> Wend Kuuni (God's Gift) (1982), 2371 -> Fletch (1985), 2979 -> Body Shots (1999), 1394 -> Raising Arizona (1987), 2772 -> Detroit Rock City (1999), 3157 -> Stuart Little (1999), 396 -> Fall Time (1995), 1526 -> Fathers' Day (1997), 3676 -> Eraserhead (1977), 2137 -> Charlotte's Web (1973), 89 -> Nick of Time (1995), 1351 -> Blood & Wine (1997), 133 -> Nueba Yol (1995), 660 -> August (1996), 3361 -> Bull Durham (1988), 2002 -> Lethal Weapon 3 (1992), 998 -> Set It Off (1996), 3756 -> Golden Bowl, The (2000), 3243 -> Encino Man (1992), 2809 -> Love Stinks (1999), 2242 -> Grandview, U.S.A. (1984), 411 -> You So Crazy (1994), 1345 -> Carrie (1976), 988 -> Grace of My Heart (1996), 2799 -> Problem Child 2 (1991), 2286 -> Fiendish Plot of Dr. Fu Manchu, The (1980), 2639 -> Mommie Dearest (1981), 3327 -> Beyond the Mat (2000), 1433 -> Machine, The (1994), 116 -> Anne Frank Remembered (1995), 2103 -> Tall Tale (1994), 2750 -> Radio Days (1987), 243 -> Gordy (1995), 1405 -> Beavis and Butt-head Do America (1996), 3226 -> Hellhounds on My Trail (1999), 1943 -> Greatest Show on Earth, The (1952), 428 -> Bronx Tale, A (1993), 1201 -> Good, The Bad and The Ugly, The (1966), 2804 -> Christmas Story, A (1983), 2965 -> Omega Code, The (1999), 1 -> Toy Story (1995), 1600 -> She's So Lovely (1997), 3870 -> Our Town (1940), 3588 -> King of Marvin Gardens, The (1972), 1104 -> Streetcar Named Desire, A (1951), 1697 -> Big Bang Theory, The (1994), 1242 -> Glory (1989), 265 -> Like Water for Chocolate (Como agua para chocolate) (1992), 3559 -> Limelight (1952), 849 -> Escape from L.A. (1996), 3606 -> On the Town (1949), 3211 -> Cry in the Dark, A (1988), 2392 -> Jack Frost (1998), 2154 -> How Stella Got Her Groove Back (1998), 3906 -> Under Suspicion (2000), 507 -> Perfect World, A (1993), 527 -> Schindler's List (1993), 312 -> Stuart Saves His Family (1995), 74 -> Bed of Roses (1996), 3492 -> Son of the Sheik, The (1926), 2718 -> Drop Dead Gorgeous (1999), 3406 -> Captain Horatio Hornblower (1951), 2205 -> Mr. & Mrs. Smith (1941), 1696 -> Bent (1997), 3184 -> Montana (1998), 206 -> Unzipped (1995), 2586 -> Goodbye, Lover (1999), 2982 -> Guardian, The (1990), 2884 -> Dog Park (1998), 1975 -> Friday the 13th Part 2 (1981), 3510 -> Frequency (2000), 1564 -> Roseanna's Grave (For Roseanna) (1997), 2762 -> Sixth Sense, The (1999), 3378 -> Ogre, The (Der Unhold) (1996), 1863 -> Major League: Back to the Minors (1998), 3435 -> Double Indemnity (1944), 2954 -> Penitentiary (1979), 307 -> Three Colors: Blue (1993), 1651 -> Telling Lies in America (1997), 1604 -> Money Talks (1997), 3899 -> Circus (2000), 2922 -> Hang 'em High (1967), 3151 -> Bat Whispers, The (1930), 2011 -> Back to the Future Part II (1989), 3097 -> Shop Around the Corner, The (1940), 3582 -> Jails, Hospitals & Hip-Hop (2000), 292 -> Outbreak (1995), 3229 -> Another Man's Poison (1952), 3011 -> They Shoot Horses, Don't They? (1969), 1206 -> Clockwork Orange, A (1971), 233 -> Exotica (1994), 452 -> Widows' Peak (1994), 6 -> Heat (1995), 920 -> Gone with the Wind (1939), 248 -> Houseguest (1994), 3857 -> Bless the Child (2000), 60 -> Indian in the Cupboard, The (1995), 380 -> True Lies (1994), 3450 -> Grumpy Old Men (1993), 3503 -> Solaris (Solyaris) (1972), 3459 -> Gothic (1986), 117 -> Young Poisoner's Handbook, The (1995), 512 -> Robert A. Heinlein's The Puppet Masters (1994), 942 -> Laura (1944), 439 -> Dangerous Game (1993), 3591 -> Mr. Mom (1983), 2910 -> Ennui, L' (1998), 3301 -> Whole Nine Yards, The (2000), 2204 -> Saboteur (1942), 3303 -> Black Tar Heroin: The Dark End of the Street (1999), 678 -> Some Folks Call It a Sling Blade (1993), 2473 -> Soul Man (1986), 270 -> Love Affair (1994), 529 -> Searching for Bobby Fischer (1993), 3567 -> Bossa Nova (1999), 2574 -> Out-of-Towners, The (1999), 1438 -> Dante's Peak (1997), 3720 -> Sunshine (1999), 661 -> James and the Giant Peach (1996), 546 -> Super Mario Bros. (1993), 793 -> My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993), 3169 -> Falcon and the Snowman, The (1984), 3265 -> Hard-Boiled (Lashou shentan) (1992), 2646 -> House of Dracula (1945), 925 -> Golden Earrings (1947), 3852 -> Tao of Steve, The (2000), 85 -> Angels and Insects (1995), 1306 -> Until the End of the World (Bis ans Ende der Welt) (1991), 201 -> Three Wishes (1995), 3531 -> All the Vermeers in New York (1990))

Let's make a data frame to visually explore the data next.

sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line => line.split("::") }.take(5)
res4: Array[Array[String]] = Array(Array(1, 1193, 5, 978300760), Array(1, 661, 3, 978302109), Array(1, 914, 3, 978301968), Array(1, 3408, 4, 978300275), Array(1, 2355, 5, 978824291))
val timedRatingsDF = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
      val fields = line.split("::")
      // format: (timestamp % 10, Rating(userId, movieId, rating))
      (fields(3).toLong, fields(0).toInt, fields(1).toInt, fields(2).toDouble)
    }.toDF("timestamp", "userId", "movieId", "rating")
timedRatingsDF: org.apache.spark.sql.DataFrame = [timestamp: bigint, userId: int ... 2 more fields]
display(timedRatingsDF)
timestamp userId movieId rating
9.7830076e8 1.0 1193.0 5.0
9.78302109e8 1.0 661.0 3.0
9.78301968e8 1.0 914.0 3.0
9.78300275e8 1.0 3408.0 4.0
9.78824291e8 1.0 2355.0 5.0
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9.782265e8 10.0 1269.0 5.0
9.79168108e8 10.0 1196.0 5.0
9.78227646e8 10.0 590.0 5.0
9.7916766e8 10.0 1197.0 5.0
9.7822563e8 10.0 1198.0 5.0
9.7916821e8 10.0 2640.0 5.0
9.78227263e8 10.0 592.0 4.0
9.78226913e8 10.0 594.0 5.0
9.78225952e8 10.0 2716.0 4.0
9.80638533e8 10.0 595.0 5.0
9.79775973e8 10.0 2717.0 4.0
9.79168108e8 10.0 2571.0 5.0
9.78228174e8 10.0 596.0 4.0
9.80638373e8 10.0 3447.0 5.0
9.78224375e8 10.0 597.0 4.0
9.7823023e8 10.0 1918.0 3.0
9.79168295e8 10.0 2140.0 5.0
9.78226287e8 10.0 1411.0 4.0
9.78230923e8 10.0 2072.0 4.0
9.78225735e8 10.0 1270.0 4.0
9.78226349e8 10.0 1271.0 5.0
9.78229295e8 10.0 1345.0 3.0
9.79775266e8 10.0 2077.0 3.0
9.78227646e8 10.0 2078.0 4.0
9.79168446e8 10.0 1276.0 3.0
9.78224375e8 10.0 743.0 3.0
9.78228408e8 10.0 671.0 3.0
9.78225922e8 10.0 1278.0 5.0
9.79168564e8 10.0 745.0 5.0
9.78226739e8 10.0 3451.0 5.0
9.782265e8 10.0 3525.0 2.0
9.78227503e8 10.0 1921.0 4.0
9.78228288e8 10.0 1923.0 5.0
9.78225599e8 10.0 1927.0 3.0
9.78227379e8 10.0 3386.0 4.0
9.78224779e8 10.0 2657.0 4.0
9.78230736e8 10.0 3388.0 3.0
9.7916847e8 10.0 1784.0 5.0
9.78227503e8 10.0 316.0 5.0
9.78228625e8 10.0 317.0 4.0
9.79775159e8 10.0 318.0 4.0
9.78229811e8 10.0 248.0 5.0
9.78226866e8 10.0 2080.0 4.0
9.78228053e8 10.0 2081.0 5.0
9.78228754e8 10.0 2082.0 3.0
9.78224549e8 10.0 1282.0 5.0
9.78227871e8 10.0 1356.0 5.0
9.80638614e8 10.0 1283.0 3.0
9.79775386e8 10.0 750.0 4.0
9.78227625e8 10.0 1357.0 5.0
9.79775738e8 10.0 2087.0 5.0
9.79167906e8 10.0 1286.0 5.0
9.78227425e8 10.0 1287.0 3.0
9.78227003e8 10.0 2804.0 5.0
9.78228601e8 10.0 3608.0 3.0
9.78227529e8 10.0 2662.0 4.0
9.79168267e8 10.0 3466.0 5.0
9.78227105e8 10.0 3100.0 3.0
9.78225682e8 10.0 2300.0 5.0
9.78228886e8 10.0 253.0 5.0
9.78226766e8 10.0 180.0 2.0
9.79168618e8 10.0 2599.0 5.0
9.78227317e8 10.0 2302.0 5.0
9.78229147e8 10.0 329.0 5.0
9.8063833e8 10.0 3034.0 4.0
9.79168036e8 10.0 3108.0 5.0
9.78225997e8 10.0 3035.0 3.0
9.78229938e8 10.0 186.0 3.0
9.78228212e8 10.0 2161.0 5.0
9.78225682e8 10.0 3037.0 5.0
9.7822882e8 10.0 2090.0 4.0
9.78228053e8 10.0 3039.0 4.0
9.78230593e8 10.0 2091.0 3.0
9.79167926e8 10.0 902.0 5.0
9.78229716e8 10.0 830.0 5.0
9.78229624e8 10.0 2093.0 4.0
9.78225735e8 10.0 1291.0 5.0
9.79775329e8 10.0 904.0 4.0
9.78229097e8 10.0 2094.0 4.0
9.79775615e8 10.0 1292.0 5.0
9.78225682e8 10.0 1293.0 4.0
9.79775364e8 10.0 2096.0 5.0
9.78225952e8 10.0 1294.0 4.0
9.7823033e8 10.0 765.0 4.0
9.78226231e8 10.0 3471.0 5.0
9.78228307e8 10.0 2746.0 5.0
9.79168267e8 10.0 1009.0 5.0
9.78226805e8 10.0 1947.0 5.0
9.782244e8 10.0 1948.0 4.0
9.78228911e8 10.0 333.0 3.0
9.79168077e8 10.0 260.0 5.0
9.78225759e8 10.0 3114.0 4.0
9.78224549e8 10.0 2312.0 5.0
9.79167945e8 10.0 339.0 5.0
9.7823023e8 10.0 1513.0 3.0
9.78229666e8 10.0 2316.0 4.0
9.78228451e8 10.0 1441.0 5.0
9.78227961e8 10.0 1517.0 3.0
9.78229517e8 10.0 1371.0 4.0
9.78227669e8 10.0 2174.0 5.0
9.78228288e8 10.0 1372.0 4.0
9.78225708e8 10.0 912.0 3.0
9.782259e8 10.0 913.0 3.0
9.7916816e8 10.0 1374.0 4.0
9.78226805e8 10.0 914.0 5.0
9.78228212e8 10.0 1375.0 4.0
9.78227215e8 10.0 915.0 5.0
9.79168132e8 10.0 1376.0 4.0
9.78229894e8 10.0 1377.0 3.0
9.79168424e8 10.0 916.0 5.0
9.78227215e8 10.0 918.0 5.0
9.78225561e8 10.0 919.0 5.0
9.78227462e8 10.0 1012.0 3.0
9.7822505e8 10.0 3481.0 4.0
9.7823054e8 10.0 2826.0 4.0
9.78225428e8 10.0 3629.0 3.0
9.78230923e8 10.0 1015.0 3.0
9.78229171e8 10.0 1016.0 5.0
9.78225735e8 10.0 1954.0 3.0
9.78227763e8 10.0 1019.0 4.0
9.78229772e8 10.0 1884.0 2.0
9.79168295e8 10.0 3489.0 4.0
9.7823033e8 10.0 1959.0 5.0
9.78229343e8 10.0 344.0 4.0
9.78230586e8 10.0 24.0 3.0
9.78227336e8 10.0 2321.0 4.0
9.79776321e8 10.0 275.0 4.0
9.78226287e8 10.0 2324.0 5.0
9.80638688e8 10.0 2252.0 5.0
9.78226147e8 10.0 277.0 3.0
9.78229336e8 10.0 1380.0 5.0
9.78227029e8 10.0 920.0 5.0
9.80638575e8 10.0 923.0 3.0
9.79168183e8 10.0 924.0 3.0
9.78227871e8 10.0 3701.0 3.0
9.78226103e8 10.0 3702.0 3.0
9.78228625e8 10.0 780.0 5.0
9.78226805e8 10.0 3703.0 2.0
9.78225952e8 10.0 1387.0 3.0
9.78226212e8 10.0 926.0 4.0
9.78228364e8 10.0 3704.0 2.0
9.78228726e8 10.0 1020.0 3.0
9.78230946e8 10.0 784.0 3.0
9.78224375e8 10.0 858.0 3.0
9.79775689e8 10.0 1022.0 5.0

Here we simply check the size of the datasets we are using

val numRatings = ratingsRDD.count
val numUsers = ratingsRDD.map(_.user).distinct.count
val numMovies = ratingsRDD.map(_.product).distinct.count

println("Got " + numRatings + " ratings from "
        + numUsers + " users on " + numMovies + " movies.")
Got 487650 ratings from 2999 users on 3615 movies.
numRatings: Long = 487650
numUsers: Long = 2999
numMovies: Long = 3615

Now that we have the dataset we need, let's make a recommender system.

Creating a Training Set, test Set and Validation Set

Before we jump into using machine learning, we need to break up the ratingsRDD dataset into three pieces:

  • A training set (RDD), which we will use to train models
  • A validation set (RDD), which we will use to choose the best model
  • A test set (RDD), which we will use for our experiments

To randomly split the dataset into the multiple groups, we can use the randomSplit() transformation. randomSplit() takes a set of splits and seed and returns multiple RDDs.

val Array(trainingRDD, validationRDD, testRDD) = ratingsRDD.randomSplit(Array(0.60, 0.20, 0.20), 0L)
trainingRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47347] at randomSplit at command-2972105651607159:1
validationRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47348] at randomSplit at command-2972105651607159:1
testRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47349] at randomSplit at command-2972105651607159:1

After splitting the dataset, your training set has about 293,000 entries and the validation and test sets each have about 97,000 entries (the exact number of entries in each dataset varies slightly due to the random nature of the randomSplit() transformation.

// let's find the exact sizes we have next
println(" training data size = " + trainingRDD.count() +
        ", validation data size = " + validationRDD.count() +
        ", test data size = " + testRDD.count() + ".")
 training data size = 292318, validation data size = 97175, test data size = 98157.

(2c) Using ALS.train()

In this part, we will use the MLlib implementation of Alternating Least Squares, ALS.train(). ALS takes a training dataset (RDD) and several parameters that control the model creation process. To determine the best values for the parameters, we will use ALS to train several models, and then we will select the best model and use the parameters from that model in the rest of this lab exercise.

The process we will use for determining the best model is as follows:

  • Pick a set of model parameters. The most important parameter to ALS.train() is the rank, which is the number of rows in the Users matrix (green in the diagram above) or the number of columns in the Movies matrix (blue in the diagram above). (In general, a lower rank will mean higher error on the training dataset, but a high rank may lead to overfitting.) We will train models with ranks of 4, 8, and 12 using the trainingRDD dataset.
  • Create a model using ALS.train(trainingRDD, rank, seed=seed, iterations=iterations, lambda_=regularizationParameter) with three parameters: an RDD consisting of tuples of the form (UserID, MovieID, rating) used to train the model, an integer rank (4, 8, or 12), a number of iterations to execute (we will use 5 for the iterations parameter), and a regularization coefficient (we will use 0.1 for the regularizationParameter).
  • For the prediction step, create an input RDD, validationForPredictRDD, consisting of (UserID, MovieID) pairs that you extract from validationRDD. You will end up with an RDD of the form: [(1, 1287), (1, 594), (1, 1270)]
  • Using the model and validationForPredictRDD, we can predict rating values by calling model.predictAll() with the validationForPredictRDD dataset, where model is the model we generated with ALS.train(). predictAll accepts an RDD with each entry in the format (userID, movieID) and outputs an RDD with each entry in the format (userID, movieID, rating).
// Build the recommendation model using ALS by fitting to the training data
// using a fixed rank=10, numIterations=10 and regularisation=0.01
val rank = 10
val numIterations = 10
val model = ALS.train(trainingRDD, rank, numIterations, 0.01)
rank: Int = 10
numIterations: Int = 10
model: org.apache.spark.mllib.recommendation.MatrixFactorizationModel = org.apache.spark.mllib.recommendation.MatrixFactorizationModel@6abb826a
// Evaluate the model on test data
val usersProductsTest = testRDD.map { case Rating(user, product, rate) =>
  (user, product)
}
usersProductsTest: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[47557] at map at command-2972105651607165:2
usersProductsTest.take(10) // Checking
res9: Array[(Int, Int)] = Array((1,2321), (1,720), (1,1545), (1,745), (1,2294), (1,1836), (1,1022), (1,150), (1,531), (2,1357))
// get the predictions on test data
val predictions =
  model.predict(usersProductsTest).map { case Rating(user, product, rate) =>
    ((user, product), rate)
  }
predictions: org.apache.spark.rdd.RDD[((Int, Int), Double)] = MapPartitionsRDD[47566] at map at command-2972105651607167:3
// find the actual ratings and join with predictions
val ratesAndPreds = testRDD.map { case Rating(user, product, rate) =>
  ((user, product), rate)
}.join(predictions)
ratesAndPreds: org.apache.spark.rdd.RDD[((Int, Int), (Double, Double))] = MapPartitionsRDD[47570] at join at command-2972105651607168:4
ratesAndPreds.take(10).map(println) // print first 10 pairs of (user,product) and (true_rating, predicted_rating)
((1147,1199),(2.0,3.8559293170362228))
((2181,1097),(2.0,4.164067445701537))
((2670,2344),(4.0,3.076550452498327))
((2156,2826),(4.0,2.3447670482871192))
((855,1455),(3.0,2.815816909694013))
((1172,1917),(3.0,3.6666224824309066))
((1635,2012),(3.0,3.3799041063244064))
((1969,592),(3.0,4.080292663763599))
((1897,1086),(4.0,4.262370135546664))
((2906,1073),(3.0,3.488097650186112))
res10: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())

Let's evaluate the model using Mean Squared Error metric.

val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
  val err = (r1 - r2)
  err * err
}.mean()
println("Mean Squared Error = " + MSE)
Mean Squared Error = 0.9692691974119817
MSE: Double = 0.9692691974119817

Can we improve the MSE by changing one of the hyper parameters?

// Build the recommendation model using ALS by fitting to the validation data
// just trying three different hyper-parameter (rank) values to optimise over
val ranks = List(4, 8, 12); 
var rank=0;
for ( rank <- ranks ){
  val numIterations = 10
  val regularizationParameter = 0.01
  val model = ALS.train(trainingRDD, rank, numIterations, regularizationParameter)

  // Evaluate the model on test data
  val usersProductsValidate = validationRDD.map { case Rating(user, product, rate) =>
                                              (user, product)
  }

  // get the predictions on test data
  val predictions = model.predict(usersProductsValidate)
                         .map { case Rating(user, product, rate)
                                     => ((user, product), rate)
    }

  // find the actual ratings and join with predictions
  val ratesAndPreds = validationRDD.map { case Rating(user, product, rate) 
                                     => ((user, product), rate)
                                   }.join(predictions)
  

  val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
    val err = (r1 - r2)
    err * err
  }.mean()
  
  println("rank and Mean Squared Error = " +  rank + " and " + MSE)
} // end of loop over ranks
rank and Mean Squared Error = 4 and 0.8483329344059245
rank and Mean Squared Error = 8 and 0.9449377020839357
rank and Mean Squared Error = 12 and 1.0217748398862294
ranks: List[Int] = List(4, 8, 12)
rank: Int = 0

Now let us try to apply this to the test data and find the MSE for the best model.

  val rank = 4
  val numIterations = 10
  val regularizationParameter = 0.01
  val model = ALS.train(trainingRDD, rank, numIterations, regularizationParameter)

  // Evaluate the model on test data
  val usersProductsTest = testRDD.map { case Rating(user, product, rate) =>
                                              (user, product)
  }

  // get the predictions on test data
  val predictions = model.predict(usersProductsTest)
                         .map { case Rating(user, product, rate)
                                     => ((user, product), rate)
    }

  // find the actual ratings and join with predictions
  val ratesAndPreds = testRDD.map { case Rating(user, product, rate) 
                                     => ((user, product), rate)
                                   }.join(predictions)

  val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
    val err = (r1 - r2)
    err * err
  }.mean()
  
  println("rank and Mean Squared Error for test data = " +  rank + " and " + MSE)
rank and Mean Squared Error for test data = 4 and 0.8553006717351811
rank: Int = 4
numIterations: Int = 10
regularizationParameter: Double = 0.01
model: org.apache.spark.mllib.recommendation.MatrixFactorizationModel = org.apache.spark.mllib.recommendation.MatrixFactorizationModel@2efe95e4
usersProductsTest: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[48449] at map at command-2972105651607175:7
predictions: org.apache.spark.rdd.RDD[((Int, Int), Double)] = MapPartitionsRDD[48458] at map at command-2972105651607175:13
ratesAndPreds: org.apache.spark.rdd.RDD[((Int, Int), (Double, Double))] = MapPartitionsRDD[48462] at join at command-2972105651607175:20
MSE: Double = 0.8553006717351811

** Potential flaws of CF **

** Areas to improve upon **

  • Works in theory but didn't manage to produce a system that takes user info and outputs suggestions
  • More complete models would include analysing the genres to give better recommendations.
  • For first time users, the program could give the top rated movies over all users.
  • Could have used bigger dataset

Project Ideas

  • Do an analysis od ALS algorithm under the hood.
  • Try to improve the model

ScaDaMaLe Course site and book

Extending spark.graphx.lib.ShortestPaths to GraphXShortestWeightedPaths

2016-2020, Ivan Sadikov and Raazesh Sainudiin

We extend Shortest Paths algorithm in Spark's GraphX Library to allow for user-specified edge-weights as an edge attribute.

This is part of Project MEP: Meme Evolution Programme and supported by databricks academic partners program.

The analysis is available in the following databricks notebook: * http://lamastex.org/lmse/mep/src/GraphXShortestWeightedPaths.html

Copyright 2016 Ivan Sadikov and Raazesh Sainudiin

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Let's modify shortest paths algorithm to allow for user-specified edge-weights

Update shortest paths algorithm to work over edge attribute of edge-weights as Double, key concepts are: - we increment map with delta, which is edge.attr - edge attribute is anything numeric, tested on Double - infinity value is not infinity, but Integer.MAX_VALUE

Modifying the following code: * https://github.com/apache/spark/blob/master/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala

Explained here: * http://note.yuhc.me/2015/03/graphx-pregel-shortest-path/

import scala.reflect.ClassTag
import org.apache.spark.graphx._

/**
 * Computes shortest weighted paths to the given set of landmark vertices, returning a graph where each
 * vertex attribute is a map containing the shortest-path distance to each reachable landmark.
 * Currently supports only Graph of [VD, Double], where VD is an arbitrary vertex type.
 */
object GraphXShortestWeightedPaths extends Serializable {
  /** Stores a map from the vertex id of a landmark to the distance to that landmark. */
  type SPMap = Map[VertexId, Double]
  // initial and infinity values, use to relax edges
  private val INITIAL = 0.0
  private val INFINITY = Int.MaxValue.toDouble

  private def makeMap(x: (VertexId, Double)*) = Map(x: _*)

  private def incrementMap(spmap: SPMap, delta: Double): SPMap = {
    spmap.map { case (v, d) => v -> (d + delta) }
  }

  private def addMaps(spmap1: SPMap, spmap2: SPMap): SPMap = {
    (spmap1.keySet ++ spmap2.keySet).map {
      k => k -> math.min(spmap1.getOrElse(k, INFINITY), spmap2.getOrElse(k, INFINITY))
    }.toMap
  }
  
  // at this point it does not really matter what vertex type is
  def run[VD](graph: Graph[VD, Double], landmarks: Seq[VertexId]): Graph[SPMap, Double] = {
    val spGraph = graph.mapVertices { (vid, attr) =>
      // initial value for itself is 0.0 as Double
      if (landmarks.contains(vid)) makeMap(vid -> INITIAL) else makeMap()
    }

    val initialMessage = makeMap()

    def vertexProgram(id: VertexId, attr: SPMap, msg: SPMap): SPMap = {
      addMaps(attr, msg)
    }

    def sendMessage(edge: EdgeTriplet[SPMap, Double]): Iterator[(VertexId, SPMap)] = {
      val newAttr = incrementMap(edge.dstAttr, edge.attr)
      if (edge.srcAttr != addMaps(newAttr, edge.srcAttr)) Iterator((edge.srcId, newAttr))
      else Iterator.empty
    }

    Pregel(spGraph, initialMessage)(vertexProgram, sendMessage, addMaps)
  }
}

println("Usage: val result = GraphXShortestWeightedPaths.run(graph, Seq(4L, 0L, 9L))")
Usage: val result = GraphXShortestWeightedPaths.run(graph, Seq(4L, 0L, 9L))
import scala.reflect.ClassTag
import org.apache.spark.graphx._
defined object GraphXShortestWeightedPaths

Generate test graph

Generate simple graph with double weights for edges

import scala.util.Random

import org.apache.spark.graphx.{Graph, VertexId}
import org.apache.spark.graphx.util.GraphGenerators

// A graph with edge attributes containing distances
val graph: Graph[Long, Double] = GraphGenerators.logNormalGraph(sc, numVertices = 10, seed=123L).mapEdges { e => 
  // to make things nicer we assign 0 distance to itself
  if (e.srcId == e.dstId) 0.0 else Random.nextDouble()
}
import scala.util.Random
import org.apache.spark.graphx.{Graph, VertexId}
import org.apache.spark.graphx.util.GraphGenerators
graph: org.apache.spark.graphx.Graph[Long,Double] = org.apache.spark.graphx.impl.GraphImpl@45dcade2
val landMarkVertexIds = Seq(4L, 0L, 9L)
val result = GraphXShortestWeightedPaths.run(graph, landMarkVertexIds)
landMarkVertexIds: Seq[Long] = List(4, 0, 9)
result: org.apache.spark.graphx.Graph[GraphXShortestWeightedPaths.SPMap,Double] = org.apache.spark.graphx.impl.GraphImpl@4bc2f186
// Found shortest paths
println(result.vertices.collect.mkString("\n"))
(0,Map(0 -> 0.0, 4 -> 0.9508628404888835, 9 -> 0.9591413283099168))
(8,Map(0 -> 0.2934032163544852, 4 -> 0.9801207594550188, 9 -> 0.8196019765599419))
(1,Map(0 -> 0.5802357924540097, 4 -> 1.2669533355545433, 9 -> 1.2752318233755766))
(9,Map(9 -> 0.0, 0 -> 0.5231864117917981, 4 -> 0.7980365238526225))
(2,Map(0 -> 0.2582682915929926, 4 -> 0.8339520209319522, 9 -> 0.5701009018719322))
(3,Map(0 -> 0.0923559237731384, 4 -> 0.18942157781466173, 9 -> 0.1977000656356951))
(4,Map(4 -> 0.0, 0 -> 0.25873464218225106, 9 -> 0.008278487821033353))
(5,Map(0 -> 0.031742669363998166, 4 -> 0.1288083234055215, 9 -> 0.13708681122655486))
(6,Map(0 -> 0.12059036542225454, 4 -> 0.8073079085227881, 9 -> 0.8155863963438215))
(7,Map(4 -> 0.22844606564222758, 9 -> 0.23672455346326093, 0 -> 0.33818821718284775))
// edges with weights, make sure to check couple of shortest paths from above
display(result.edges.toDF)
srcId dstId attr
0.0 0.0 0.0
0.0 1.0 0.6721979874068958
0.0 4.0 0.9508628404888835
1.0 1.0 0.0
1.0 6.0 0.45964542703175515
1.0 6.0 0.7349975451261668
2.0 0.0 0.2582682915929926
2.0 1.0 0.9280493920155959
2.0 4.0 0.8339520209319522
2.0 6.0 0.26091386534774674
2.0 6.0 0.9481996224496678
2.0 9.0 0.5701009018719322
3.0 2.0 0.8893068591814861
3.0 3.0 0.0
3.0 5.0 6.061325440914023e-2
3.0 7.0 0.26415564719487117
3.0 8.0 0.5847037097670544
4.0 0.0 0.373044758393941
4.0 3.0 0.16637871840911267
4.0 5.0 0.8133138055926398
4.0 7.0 0.5329270151953733
4.0 7.0 0.7076376180464232
4.0 9.0 0.3590440724653333
4.0 9.0 8.278487821033353e-3
5.0 0.0 3.1742669363998166e-2
5.0 1.0 0.4799303618281624
5.0 2.0 0.9373917774347413
5.0 3.0 5.426676615013515e-2
5.0 4.0 0.1288083234055215
5.0 5.0 0.0
6.0 0.0 0.12059036542225454
6.0 1.0 1.867282877196641e-2
6.0 2.0 0.9882878315838144
6.0 3.0 0.944141072288605
6.0 5.0 0.6784995851172666
6.0 6.0 0.0
6.0 6.0 0.0
6.0 6.0 0.0
6.0 8.0 0.16318250935765966
7.0 2.0 7.991992558985517e-2
7.0 2.0 0.7824158528877193
7.0 2.0 0.17715746355547157
7.0 4.0 0.7655621066580156
7.0 4.0 0.2580382514924928
7.0 4.0 0.22844606564222758
7.0 8.0 0.750024180696321
7.0 9.0 0.9228699021998864
8.0 1.0 0.351758945103697
8.0 2.0 0.24950107468800975
8.0 3.0 0.9928995269181429
8.0 6.0 0.17281285093223064
9.0 0.0 0.5838217791812277
9.0 2.0 0.26491812019880556
9.0 4.0 0.853261480233851
9.0 7.0 0.5695904582103949
display(graph.edges.toDF) // this is the directed weighted edge of the graph
srcId dstId attr
0.0 0.0 0.0
0.0 1.0 0.2473870305881326
0.0 4.0 0.7593678671727498
1.0 1.0 0.0
1.0 6.0 0.4721793531500751
1.0 6.0 0.7565157046946671
2.0 0.0 0.8601376666144385
2.0 1.0 0.15474243507003416
2.0 4.0 0.17585136043466876
2.0 6.0 0.9762506825428003
2.0 6.0 0.8022953138023812
2.0 9.0 0.8898859140300339
3.0 2.0 0.23688722482284008
3.0 3.0 0.0
3.0 5.0 0.6621552329080445
3.0 7.0 0.6189436789554128
3.0 8.0 0.7648758038805598
4.0 0.0 0.9568298106188533
4.0 3.0 0.41779642900620517
4.0 5.0 0.2897812470698585
4.0 7.0 0.9845657600597383
4.0 7.0 0.6752427687212571
4.0 9.0 0.23033960072928972
4.0 9.0 0.6688651818696816
5.0 0.0 0.4544165543058408
5.0 1.0 3.778698342738085e-2
5.0 2.0 0.8383018223597426
5.0 3.0 0.7890454146717444
5.0 4.0 0.3471895990573223
5.0 5.0 0.0
6.0 0.0 0.2431403242371375
6.0 1.0 0.6109097064072662
6.0 2.0 0.7701858934760617
6.0 3.0 0.12066591357902912
6.0 5.0 0.5034427240618765
6.0 6.0 0.0
6.0 6.0 0.0
6.0 6.0 0.0
6.0 8.0 0.616288715486008
7.0 2.0 7.870712177892414e-2
7.0 2.0 0.5147452602931151
7.0 2.0 0.8214716030697653
7.0 4.0 0.25578984521277015
7.0 4.0 0.3583207455757458
7.0 4.0 8.320072407893553e-2
7.0 8.0 0.36976177580734204
7.0 9.0 0.5704097005469446
8.0 1.0 0.20123008667210507
8.0 2.0 0.7558945801315478
8.0 3.0 0.5592884924812593
8.0 6.0 0.5437831714308268
9.0 0.0 0.7347489314741307
9.0 2.0 0.2611206797801059
9.0 4.0 0.6543183939764342
9.0 7.0 0.6768117581780281
// now let us collect the shortest distance between every vertex and every landmark vertex
// to manipulate scala maps that are vertices of the result see: http://docs.scala-lang.org/overviews/collections/maps.html
// a quick point: http://stackoverflow.com/questions/28769367/scala-map-a-map-to-list-of-tuples
val shortestDistsVertex2Landmark = result.vertices.flatMap(GxSwpSPMap => {
  GxSwpSPMap._2.toSeq.map(x => (GxSwpSPMap._1, x._1, x._2)) // to get triples: vertex, landmarkVertex, shortest_distance
})
shortestDistsVertex2Landmark: org.apache.spark.rdd.RDD[(org.apache.spark.graphx.VertexId, org.apache.spark.graphx.VertexId, Double)] = MapPartitionsRDD[3128] at flatMap at command-2744653148555415:4
shortestDistsVertex2Landmark.collect.mkString("\n")
res4: String =
(0,0,0.0)
(0,4,0.9508628404888835)
(0,9,0.9591413283099168)
(8,0,0.2934032163544852)
(8,4,0.9801207594550188)
(8,9,0.8196019765599419)
(1,0,0.5802357924540097)
(1,4,1.2669533355545433)
(1,9,1.2752318233755766)
(9,9,0.0)
(9,0,0.5231864117917981)
(9,4,0.7980365238526225)
(2,0,0.2582682915929926)
(2,4,0.8339520209319522)
(2,9,0.5701009018719322)
(3,0,0.0923559237731384)
(3,4,0.18942157781466173)
(3,9,0.1977000656356951)
(4,4,0.0)
(4,0,0.25873464218225106)
(4,9,0.008278487821033353)
(5,0,0.031742669363998166)
(5,4,0.1288083234055215)
(5,9,0.13708681122655486)
(6,0,0.12059036542225454)
(6,4,0.8073079085227881)
(6,9,0.8155863963438215)
(7,4,0.22844606564222758)
(7,9,0.23672455346326093)
(7,0,0.33818821718284775)

Let's make a DataFrame for visualizing pairwise matrix plots

We want to make 4 columns in this example as follows (note actual values change for each realisation of graph!):

landmark_Id1 ("0"),   landmarkID2 ("4"), landmarkId3 ("9"),  srcVertexId
------------------------------------------------------------------------
0.0,                  0.7425..,          0.8718,                0
0.924...,             1.2464..,          1.0472,                1
...
// http://alvinalexander.com/scala/how-to-sort-map-in-scala-key-value-sortby-sortwith
// we need this to make sure that the maps are ordered by the keys for ensuring unique column values
import scala.collection.immutable.ListMap
import sqlContext.implicits._
import scala.collection.immutable.ListMap
import sqlContext.implicits._
 // recall our landmark vertices in landMarkVertexIds. let's use their Strings for names
val unorderedNamedLandmarkVertices = landMarkVertexIds.map(id => (id, id.toString) )
val orderedNamedLandmarkVertices = ListMap(unorderedNamedLandmarkVertices.sortBy(_._1):_*)
val orderedLandmarkVertexNames = orderedNamedLandmarkVertices.toSeq.map(x => x._2)
orderedLandmarkVertexNames.mkString(", ")
unorderedNamedLandmarkVertices: Seq[(Long, String)] = List((4,4), (0,0), (9,9))
orderedNamedLandmarkVertices: scala.collection.immutable.ListMap[Long,String] = ListMap(0 -> 0, 4 -> 4, 9 -> 9)
orderedLandmarkVertexNames: Seq[String] = Vector(0, 4, 9)
res5: String = 0, 4, 9
// this is going to be our column names
val columnNames:Seq[String] = orderedLandmarkVertexNames :+ "srcVertexId"
columnNames: Seq[String] = Vector(0, 4, 9, srcVertexId)
// a case class to make a data-frame quickly from the result
case class SeqOfDoublesAndsrcVertexId(shortestDistances: Seq[Double], srcVertexId: VertexId)
defined class SeqOfDoublesAndsrcVertexId
val shortestDistsSeqFromVertex2Landmark2DF = result.vertices.map(GxSwpSPMap => {
  //GxSwpSPMap._2.toSeq.map(x => (GxSwpSPMap._1, x._1, x._2)) // from before to get triples: vertex, landmarkVertex, shortest_distance
  val v = GxSwpSPMap._1
  val a = ListMap(GxSwpSPMap._2.toSeq.sortBy(_._1):_*).toSeq.map(x => x._2)
  val d = (a,v)
  d
}).map(x => SeqOfDoublesAndsrcVertexId(x._1, x._2)).toDF()
shortestDistsSeqFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [shortestDistances: array<double>, srcVertexId: bigint]
display(shortestDistsSeqFromVertex2Landmark2DF) // but this dataframe needs the first column exploded into 3 columns

Now we want to make separate columns for each distance in the Sequence in column 'shortestDistances'.

Let us use the following ideas for this: * https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/3741049972324885/2662535171379268/4413065072037724/latest.html

// this is from https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/3741049972324885/2662535171379268/4413065072037724/latest.html
import org.apache.spark.sql.{Column, DataFrame}
import org.apache.spark.sql.functions.{lit, udf}

// UDF to extract i-th element from array column
//val elem = udf((x: Seq[Int], y: Int) => x(y))
val elem = udf((x: Seq[Double], y: Int) => x(y)) // modified for Sequence of Doubles

// Method to apply 'elem' UDF on each element, requires knowing length of sequence in advance
def split(col: Column, len: Int): Seq[Column] = {
  for (i <- 0 until len) yield { elem(col, lit(i)).as(s"$col($i)") }
}

// Implicit conversion to make things nicer to use, e.g. 
// select(Column, Seq[Column], Column) is converted into select(Column*) flattening sequences
implicit class DataFrameSupport(df: DataFrame) {
  def select(cols: Any*): DataFrame = {
    var buffer: Seq[Column] = Seq.empty
    for (col <- cols) {
      if (col.isInstanceOf[Seq[_]]) {
        buffer = buffer ++ col.asInstanceOf[Seq[Column]]
      } else {
        buffer = buffer :+ col.asInstanceOf[Column]
      }
    }
    df.select(buffer:_*)
  }
}
import org.apache.spark.sql.{Column, DataFrame}
import org.apache.spark.sql.functions.{lit, udf}
elem: org.apache.spark.sql.expressions.UserDefinedFunction = SparkUserDefinedFunction($Lambda$7813/1969990681@1a441b0e,DoubleType,List(Some(class[value[0]: array<double>]), Some(class[value[0]: int])),None,false,true)
split: (col: org.apache.spark.sql.Column, len: Int)Seq[org.apache.spark.sql.Column]
defined class DataFrameSupport
val shortestDistsFromVertex2Landmark2DF = shortestDistsSeqFromVertex2Landmark2DF.select(split($"shortestDistances", 3), $"srcVertexId")
shortestDistsFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [shortestDistances(0): double, shortestDistances(1): double ... 2 more fields]
display(shortestDistsFromVertex2Landmark2DF)
shortestDistances(0) shortestDistances(1) shortestDistances(2) srcVertexId
0.0 0.9508628404888835 0.9591413283099168 0.0
0.2934032163544852 0.9801207594550188 0.8196019765599419 8.0
0.5802357924540097 1.2669533355545433 1.2752318233755766 1.0
0.5231864117917981 0.7980365238526225 0.0 9.0
0.2582682915929926 0.8339520209319522 0.5701009018719322 2.0
9.23559237731384e-2 0.18942157781466173 0.1977000656356951 3.0
0.25873464218225106 0.0 8.278487821033353e-3 4.0
3.1742669363998166e-2 0.1288083234055215 0.13708681122655486 5.0
0.12059036542225454 0.8073079085227881 0.8155863963438215 6.0
0.33818821718284775 0.22844606564222758 0.23672455346326093 7.0
// now let's give it our names based on the landmark vertex Ids
val shortestDistsFromVertex2Landmark2DF = shortestDistsSeqFromVertex2Landmark2DF.select(split($"shortestDistances", 3), $"srcVertexId").toDF(columnNames:_*)
shortestDistsFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [0: double, 4: double ... 2 more fields]
display(shortestDistsFromVertex2Landmark2DF)
0 4 9 srcVertexId
0.0 0.9508628404888835 0.9591413283099168 0.0
0.2934032163544852 0.9801207594550188 0.8196019765599419 8.0
0.5802357924540097 1.2669533355545433 1.2752318233755766 1.0
0.5231864117917981 0.7980365238526225 0.0 9.0
0.2582682915929926 0.8339520209319522 0.5701009018719322 2.0
9.23559237731384e-2 0.18942157781466173 0.1977000656356951 3.0
0.25873464218225106 0.0 8.278487821033353e-3 4.0
3.1742669363998166e-2 0.1288083234055215 0.13708681122655486 5.0
0.12059036542225454 0.8073079085227881 0.8155863963438215 6.0
0.33818821718284775 0.22844606564222758 0.23672455346326093 7.0
display(shortestDistsFromVertex2Landmark2DF.select($"0",$"4",$"9"))
0 4 9
0.0 0.9508628404888835 0.9591413283099168
0.2934032163544852 0.9801207594550188 0.8196019765599419
0.5802357924540097 1.2669533355545433 1.2752318233755766
0.5231864117917981 0.7980365238526225 0.0
0.2582682915929926 0.8339520209319522 0.5701009018719322
9.23559237731384e-2 0.18942157781466173 0.1977000656356951
0.25873464218225106 0.0 8.278487821033353e-3
3.1742669363998166e-2 0.1288083234055215 0.13708681122655486
0.12059036542225454 0.8073079085227881 0.8155863963438215
0.33818821718284775 0.22844606564222758 0.23672455346326093

ScaDaMaLe Course site and book

Financial Fraud Detection using Decision Tree Machine Learning Models

This notebooks is the accompaniment to the following databricks blog: - https://databricks.com/blog/2019/05/02/detecting-financial-fraud-at-scale-with-decision-trees-and-mlflow-on-databricks.html

This is an exercise in self-learning. Here you will learn to use mlflow in pyspark to keep track of your experiments.

Also you may have to adapt it for Spark 3.x if needed.

In this notebook, we will showcase the use of decision tree ML models to perform financial fraud detection.


Source Data

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle. To load the dataset yourself, please download it to your local machine from Kaggle and then import the data via Import Data: Azure | AWS.


Dictionary

This is the column definition of the referenced sythentic dataset.

Column NameDescription
stepmaps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).
typeCASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.
amountamount of the transaction in local currency.
nameOrigcustomer who started the transaction
oldbalanceOrginitial balance before the transaction
newbalanceOrignew balance after the transaction
nameDestcustomer who is the recipient of the transaction
oldbalanceDestinitial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).
newbalanceDestnew balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

# Configure MLflow Experiment
mlflow_experiment_id = 866112

# Including MLflow
import mlflow
import mlflow.spark

import os
print("MLflow Version: %s" % mlflow.__version__)
MLflow Version: 0.8.2
# Create df DataFrame which contains our simulated financial fraud detection dataset
df = spark.sql("select step, type, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest from sim_fin_fraud_detection")
# Review the schema of your data 
df.printSchema()
root
 |-- step: integer (nullable = true)
 |-- type: string (nullable = true)
 |-- amount: double (nullable = true)
 |-- nameOrig: string (nullable = true)
 |-- oldbalanceOrg: double (nullable = true)
 |-- newbalanceOrig: double (nullable = true)
 |-- nameDest: string (nullable = true)
 |-- oldbalanceDest: double (nullable = true)
 |-- newbalanceDest: double (nullable = true)

Calculate Differences between Originating and Destination Balanaces

With the following PySpark DataFrame query, we will calculate the following columns:

New ColumnDefinition
orgDiffDifference between the originating balance
destDiffDifference between the destination balance
# Calculate the differences between originating and destination balances
df = df.withColumn("orgDiff", df.newbalanceOrig - df.oldbalanceOrg).withColumn("destDiff", df.newbalanceDest - df.oldbalanceDest)

# Create temporary view
df.createOrReplaceTempView("financials")
# Review the new table (including the origination and destiation differences)
display(df)
step type amount nameOrig oldbalanceOrg newbalanceOrig nameDest oldbalanceDest newbalanceDest orgDiff destDiff
1.0 PAYMENT 9839.64 C1231006815 170136.0 160296.36 M1979787155 0.0 0.0 -9839.640000000014 0.0
1.0 PAYMENT 1864.28 C1666544295 21249.0 19384.72 M2044282225 0.0 0.0 -1864.2799999999988 0.0
1.0 TRANSFER 181.0 C1305486145 181.0 0.0 C553264065 0.0 0.0 -181.0 0.0
1.0 CASH_OUT 181.0 C840083671 181.0 0.0 C38997010 21182.0 0.0 -181.0 -21182.0
1.0 PAYMENT 11668.14 C2048537720 41554.0 29885.86 M1230701703 0.0 0.0 -11668.14 0.0
1.0 PAYMENT 7817.71 C90045638 53860.0 46042.29 M573487274 0.0 0.0 -7817.709999999999 0.0
1.0 PAYMENT 7107.77 C154988899 183195.0 176087.23 M408069119 0.0 0.0 -7107.7699999999895 0.0
1.0 PAYMENT 7861.64 C1912850431 176087.23 168225.59 M633326333 0.0 0.0 -7861.640000000014 0.0
1.0 PAYMENT 4024.36 C1265012928 2671.0 0.0 M1176932104 0.0 0.0 -2671.0 0.0
1.0 DEBIT 5337.77 C712410124 41720.0 36382.23 C195600860 41898.0 40348.79 -5337.769999999997 -1549.2099999999991
1.0 DEBIT 9644.94 C1900366749 4465.0 0.0 C997608398 10845.0 157982.12 -4465.0 147137.12
1.0 PAYMENT 3099.97 C249177573 20771.0 17671.03 M2096539129 0.0 0.0 -3099.970000000001 0.0
1.0 PAYMENT 2560.74 C1648232591 5070.0 2509.26 M972865270 0.0 0.0 -2560.74 0.0
1.0 PAYMENT 11633.76 C1716932897 10127.0 0.0 M801569151 0.0 0.0 -10127.0 0.0
1.0 PAYMENT 4098.78 C1026483832 503264.0 499165.22 M1635378213 0.0 0.0 -4098.780000000028 0.0
1.0 CASH_OUT 229133.94 C905080434 15325.0 0.0 C476402209 5083.0 51513.44 -15325.0 46430.44
1.0 PAYMENT 1563.82 C761750706 450.0 0.0 M1731217984 0.0 0.0 -450.0 0.0
1.0 PAYMENT 1157.86 C1237762639 21156.0 19998.14 M1877062907 0.0 0.0 -1157.8600000000006 0.0
1.0 PAYMENT 671.64 C2033524545 15123.0 14451.36 M473053293 0.0 0.0 -671.6399999999994 0.0
1.0 TRANSFER 215310.3 C1670993182 705.0 0.0 C1100439041 22425.0 0.0 -705.0 -22425.0
1.0 PAYMENT 1373.43 C20804602 13854.0 12480.57 M1344519051 0.0 0.0 -1373.4300000000003 0.0
1.0 DEBIT 9302.79 C1566511282 11299.0 1996.21 C1973538135 29832.0 16896.7 -9302.79 -12935.3
1.0 DEBIT 1065.41 C1959239586 1817.0 751.59 C515132998 10330.0 0.0 -1065.4099999999999 -10330.0
1.0 PAYMENT 3876.41 C504336483 67852.0 63975.59 M1404932042 0.0 0.0 -3876.4100000000035 0.0
1.0 TRANSFER 311685.89 C1984094095 10835.0 0.0 C932583850 6267.0 2719172.89 -10835.0 2712905.89
1.0 PAYMENT 6061.13 C1043358826 443.0 0.0 M1558079303 0.0 0.0 -443.0 0.0
1.0 PAYMENT 9478.39 C1671590089 116494.0 107015.61 M58488213 0.0 0.0 -9478.39 0.0
1.0 PAYMENT 8009.09 C1053967012 10968.0 2958.91 M295304806 0.0 0.0 -8009.09 0.0
1.0 PAYMENT 8901.99 C1632497828 2958.91 0.0 M33419717 0.0 0.0 -2958.91 0.0
1.0 PAYMENT 9920.52 C764826684 0.0 0.0 M1940055334 0.0 0.0 0.0 0.0
1.0 PAYMENT 3448.92 C2103763750 0.0 0.0 M335107734 0.0 0.0 0.0 0.0
1.0 PAYMENT 4206.84 C215078753 0.0 0.0 M1757317128 0.0 0.0 0.0 0.0
1.0 PAYMENT 5885.56 C840514538 0.0 0.0 M1804441305 0.0 0.0 0.0 0.0
1.0 PAYMENT 5307.88 C1768242710 0.0 0.0 M1971783162 0.0 0.0 0.0 0.0
1.0 PAYMENT 5031.22 C247113419 0.0 0.0 M151442075 0.0 0.0 0.0 0.0
1.0 PAYMENT 24213.67 C1238616099 0.0 0.0 M70695990 0.0 0.0 0.0 0.0
1.0 PAYMENT 8603.42 C1608633989 253.0 0.0 M1615617512 0.0 0.0 -253.0 0.0
1.0 PAYMENT 2791.42 C923341586 300481.0 297689.58 M107994825 0.0 0.0 -2791.4199999999837 0.0
1.0 PAYMENT 7413.54 C1470868839 297689.58 290276.03 M1426725223 0.0 0.0 -7413.549999999988 0.0
1.0 PAYMENT 3295.19 C711197015 233633.0 230337.81 M1384454980 0.0 0.0 -3295.1900000000023 0.0
1.0 PAYMENT 1684.81 C1481594086 297.0 0.0 M1569435561 0.0 0.0 -297.0 0.0
1.0 DEBIT 5758.59 C1466917878 32604.0 26845.41 C1297685781 209699.0 16997.22 -5758.59 -192701.78
1.0 CASH_OUT 110414.71 C768216420 26845.41 0.0 C1509514333 288800.0 2415.16 -26845.41 -286384.84
1.0 PAYMENT 7823.46 C260084831 998.0 0.0 M267814113 0.0 0.0 -998.0 0.0
1.0 PAYMENT 5086.48 C598357562 0.0 0.0 M1593224710 0.0 0.0 0.0 0.0
1.0 PAYMENT 5281.48 C1440738283 152019.0 146737.52 M1849015357 0.0 0.0 -5281.4800000000105 0.0
1.0 PAYMENT 13875.98 C484199463 15818.0 1942.02 M2008106788 0.0 0.0 -13875.98 0.0
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1.0 CASH_OUT 131991.26 C1396385390 0.0 0.0 C1971489295 1145221.51 0.0 0.0 -1145221.51
1.0 CASH_OUT 65025.3 C114414807 0.0 0.0 C977993101 133181.81 965870.05 0.0 832688.24
1.0 CASH_OUT 82054.36 C144699438 0.0 0.0 C1526298704 174795.14 32092.07 0.0 -142703.07
1.0 CASH_OUT 69708.79 C1751500625 0.0 0.0 C1335050193 71982.88 353532.56 0.0 281549.68
1.0 CASH_OUT 9104.26 C1650955365 0.0 0.0 C990355670 10623.0 19727.26 0.0 9104.259999999998
1.0 CASH_OUT 92553.64 C1788380050 0.0 0.0 C1182461167 297655.51 23508.22 0.0 -274147.29000000004
1.0 CASH_OUT 66312.08 C493944943 0.0 0.0 C1750905143 544421.33 424250.45 0.0 -120170.87999999995
1.0 CASH_OUT 142282.63 C1533547487 0.0 0.0 C453211571 805331.66 3461666.05 0.0 2656334.3899999997
1.0 CASH_OUT 114499.14 C99770475 0.0 0.0 C1899073220 129297.57 420946.86 0.0 291649.29
1.0 CASH_OUT 76635.24 C727250772 0.0 0.0 C357863579 102696.35 92307.65 0.0 -10388.700000000012
1.0 CASH_OUT 132574.0 C1052768296 0.0 0.0 C977993101 198207.11 965870.05 0.0 767662.9400000001
1.0 CASH_OUT 39485.21 C626193099 0.0 0.0 C1883840933 9987286.56 1.687464309e7 0.0 6887356.529999999
1.0 CASH_OUT 236825.42 C859123506 0.0 0.0 C1899073220 243796.7 420946.86 0.0 177150.15999999997
1.0 CASH_OUT 327066.19 C1597742167 0.0 0.0 C453211571 947614.29 3461666.05 0.0 2514051.76
1.0 CASH_OUT 156819.6 C503595296 0.0 0.0 C1318822808 438635.96 500631.71 0.0 61995.75
1.0 CASH_OUT 13136.44 C754072705 0.0 0.0 C1023714065 1525395.97 1412484.09 0.0 -112911.87999999989
1.0 PAYMENT 4306.89 C908384914 256849.49 252542.6 M905847077 0.0 0.0 -4306.889999999985 0.0
1.0 PAYMENT 8309.52 C156199931 252542.6 244233.08 M878991463 0.0 0.0 -8309.520000000019 0.0
1.0 PAYMENT 5363.35 C612693043 81503.83 76140.48 M1622613647 0.0 0.0 -5363.350000000006 0.0
1.0 PAYMENT 132.42 C307605969 76140.48 76008.06 M73688220 0.0 0.0 -132.41999999999825 0.0
1.0 PAYMENT 19644.08 C130166095 76008.06 56363.98 M1599674462 0.0 0.0 -19644.079999999994 0.0
1.0 PAYMENT 895.24 C1623060829 56363.98 55468.74 M1205483858 0.0 0.0 -895.2400000000052 0.0
1.0 PAYMENT 7641.08 C1373949107 55468.74 47827.66 M1189651769 0.0 0.0 -7641.0799999999945 0.0
1.0 PAYMENT 31188.22 C1230013344 47827.66 16639.44 M1607869297 0.0 0.0 -31188.220000000005 0.0
1.0 PAYMENT 13954.75 C681639276 16639.44 2684.69 M727860268 0.0 0.0 -13954.749999999998 0.0
1.0 PAYMENT 10497.33 C1717739363 2684.69 0.0 M138536309 0.0 0.0 -2684.69 0.0
1.0 PAYMENT 5843.75 C665137804 0.0 0.0 M509559152 0.0 0.0 0.0 0.0
1.0 PAYMENT 7260.2 C1815370847 0.0 0.0 M1801021153 0.0 0.0 0.0 0.0
1.0 PAYMENT 10285.86 C1709578324 0.0 0.0 M22446425 0.0 0.0 0.0 0.0
1.0 PAYMENT 6249.78 C1338958728 0.0 0.0 M1870723838 0.0 0.0 0.0 0.0
1.0 PAYMENT 848.74 C988904418 0.0 0.0 M261650860 0.0 0.0 0.0 0.0
1.0 PAYMENT 6993.7 C938613108 0.0 0.0 M1598898814 0.0 0.0 0.0 0.0
1.0 PAYMENT 795.29 C1850874910 10360.0 9564.71 M575360353 0.0 0.0 -795.2900000000009 0.0
1.0 PAYMENT 9191.46 C97901029 0.0 0.0 M809383315 0.0 0.0 0.0 0.0
1.0 CASH_OUT 132038.41 C1324514662 0.0 0.0 C998351292 230103.96 1015132.48 0.0 785028.52
1.0 PAYMENT 6495.27 C908722588 22040.0 15544.73 M496757837 0.0 0.0 -6495.27 0.0
1.0 PAYMENT 1679.68 C1548271808 15544.73 13865.06 M17600354 0.0 0.0 -1679.67 0.0
1.0 TRANSFER 47477.97 C1733056574 6066.0 0.0 C1629911510 144658.38 15375.37 -6066.0 -129283.01000000001
1.0 DEBIT 1260.62 C636959006 1483.0 222.38 C1686100174 10086.0 9837.06 -1260.62 -248.9400000000005
1.0 PAYMENT 15034.23 C1059300256 40458.0 25423.77 M1521568953 0.0 0.0 -15034.23 0.0
1.0 PAYMENT 3238.39 C1605988985 28773.0 25534.61 M1134202713 0.0 0.0 -3238.3899999999994 0.0
1.0 CASH_OUT 251241.14 C1636178473 25534.61 0.0 C997608398 523053.03 157982.12 -25534.61 -365070.91000000003
1.0 PAYMENT 3644.12 C822232612 1230.0 0.0 M633131207 0.0 0.0 -1230.0 0.0
1.0 CASH_OUT 283123.48 C852190062 497409.0 214285.52 C1831477404 16872.0 247063.16 -283123.48 230191.16
1.0 PAYMENT 2851.83 C1674403916 10646.0 7794.17 M1088239991 0.0 0.0 -2851.83 0.0
1.0 CASH_IN 94726.64 C61137731 69854.13 164580.77 C476800120 129704.88 49864.36 94726.63999999998 -79840.52
1.0 PAYMENT 17020.69 C1780293706 164580.77 147560.08 M1658511941 0.0 0.0 -17020.690000000002 0.0
1.0 PAYMENT 6523.19 C1264941544 147560.08 141036.89 M1878992188 0.0 0.0 -6523.189999999973 0.0
1.0 PAYMENT 1383.56 C1818449913 5309.0 3925.44 M1495161082 0.0 0.0 -1383.56 0.0
1.0 DEBIT 1580.66 C961859592 31156.0 29575.34 C1556995360 12298.0 0.0 -1580.6599999999999 -12298.0
1.0 PAYMENT 2466.95 C798278875 34978.24 32511.29 M1497268815 0.0 0.0 -2466.949999999997 0.0
1.0 PAYMENT 1017.85 C1926027290 32511.29 31493.45 M1837601499 0.0 0.0 -1017.8400000000001 0.0
1.0 PAYMENT 2270.41 C1690050988 60640.12 58369.71 M1228798862 0.0 0.0 -2270.4100000000035 0.0
1.0 CASH_OUT 102265.88 C1562764987 58369.71 0.0 C392292416 1438958.75 3420103.09 -58369.71 1981144.3399999999
1.0 DEBIT 13758.63 C216376974 29858.0 16099.37 C1219161283 21305.0 0.0 -13758.63 -21305.0
1.0 CASH_IN 5221.77 C164714495 1046.0 6267.77 C317071334 0.0 139555.44 5221.77 139555.44
1.0 PAYMENT 12256.66 C789982400 20800.0 8543.34 M1576277927 0.0 0.0 -12256.66 0.0
1.0 PAYMENT 8227.3 C2080643905 6207.0 0.0 M2137642385 0.0 0.0 -6207.0 0.0
1.0 PAYMENT 527.15 C1627883152 72421.0 71893.85 M481553464 0.0 0.0 -527.1499999999942 0.0
1.0 PAYMENT 950.25 C1076966140 204944.0 203993.75 M1908781622 0.0 0.0 -950.25 0.0
1.0 PAYMENT 12728.46 C749443480 83636.0 70907.54 M796553753 0.0 0.0 -12728.460000000006 0.0
1.0 PAYMENT 1002.96 C1721045976 8325.0 7322.04 M975001918 0.0 0.0 -1002.96 0.0
1.0 PAYMENT 6917.1 C1250582716 13555.0 6637.9 M907815246 0.0 0.0 -6917.1 0.0
1.0 PAYMENT 1977.17 C2019157894 43619.0 41641.83 M677217562 0.0 0.0 -1977.1699999999983 0.0
1.0 PAYMENT 9314.84 C827035437 104536.0 95221.16 M1716164115 0.0 0.0 -9314.839999999997 0.0
1.0 PAYMENT 1157.05 C1360541835 52867.0 51709.95 M363397863 0.0 0.0 -1157.050000000003 0.0
1.0 DEBIT 3766.99 C2021053848 16600.0 12833.01 C1123629720 146166.67 46393.85 -3766.99 -99772.82
1.0 PAYMENT 3464.41 C1245593227 5548.0 2083.59 M1525844775 0.0 0.0 -3464.41 0.0
1.0 CASH_OUT 68662.06 C118555812 2083.59 0.0 C1740000325 145843.39 1363368.51 -2083.59 1217525.12
1.0 DEBIT 8679.29 C1233505227 150592.0 141912.71 C1877453512 21056.0 0.0 -8679.290000000008 -21056.0
1.0 PAYMENT 15266.05 C1928621590 13679.0 0.0 M1016162524 0.0 0.0 -13679.0 0.0
1.0 PAYMENT 8348.75 C858423246 0.0 0.0 M1419125235 0.0 0.0 0.0 0.0
1.0 PAYMENT 8419.73 C88301993 0.0 0.0 M841166421 0.0 0.0 0.0 0.0
1.0 PAYMENT 6688.74 C1377301456 9207.0 2518.26 M938199512 0.0 0.0 -6688.74 0.0
1.0 PAYMENT 1979.94 C691779749 82034.0 80054.06 M37841489 0.0 0.0 -1979.9400000000023 0.0
1.0 PAYMENT 9574.31 C1873121466 52463.0 42888.69 M115945887 0.0 0.0 -9574.309999999998 0.0
1.0 PAYMENT 5287.68 C1927499639 11424.0 6136.32 M2079961240 0.0 0.0 -5287.68 0.0
1.0 PAYMENT 10627.18 C949673757 6136.32 0.0 M1999664216 0.0 0.0 -6136.32 0.0
1.0 PAYMENT 5809.78 C532677950 99884.0 94074.22 M688593710 0.0 0.0 -5809.779999999999 0.0
1.0 TRANSFER 411363.54 C1200048933 39951.0 0.0 C1049817027 52170.0 60738.03 -39951.0 8568.029999999999
1.0 PAYMENT 4086.5 C862025017 0.0 0.0 M739737502 0.0 0.0 0.0 0.0
1.0 PAYMENT 19741.22 C1572127577 81762.38 62021.17 M546796969 0.0 0.0 -19741.210000000006 0.0
1.0 PAYMENT 5270.72 C417797183 62021.17 56750.45 M261845810 0.0 0.0 -5270.720000000001 0.0
1.0 CASH_IN 55827.09 C1959708563 9907.0 65734.09 C1850180796 19873.0 1268668.92 55827.09 1248795.92
1.0 CASH_OUT 174068.82 C1877986974 65734.09 0.0 C33524623 1905641.8 1517262.16 -65734.09 -388379.64000000013
1.0 TRANSFER 1277212.77 C1334405552 1277212.77 0.0 C431687661 0.0 0.0 -1277212.77 0.0
1.0 CASH_OUT 1277212.77 C467632528 1277212.77 0.0 C716083600 0.0 2444985.19 -1277212.77 2444985.19
1.0 CASH_IN 24105.5 C422522663 144.0 24249.5 C644345897 20875.0 0.0 24105.5 -20875.0
1.0 PAYMENT 6854.06 C145066402 19853.0 12998.94 M207988207 0.0 0.0 -6854.0599999999995 0.0
1.0 CASH_OUT 111899.09 C1800500978 12998.94 0.0 C1883840933 1.002677177e7 1.687464309e7 -12998.94 6847871.32
1.0 PAYMENT 1231.79 C191310504 20792.0 19560.21 M799755007 0.0 0.0 -1231.7900000000009 0.0
1.0 PAYMENT 3380.1 C247515192 19560.21 16180.11 M1850137076 0.0 0.0 -3380.0999999999985 0.0
1.0 DEBIT 2869.65 C1556520190 8768.0 5898.35 C1664439369 15485.0 5484.37 -2869.6499999999996 -10000.630000000001
1.0 PAYMENT 1658.87 C701487403 16543.0 14884.13 M566641954 0.0 0.0 -1658.8700000000008 0.0
1.0 PAYMENT 823.36 C2039984535 14884.13 14060.77 M299819831 0.0 0.0 -823.3599999999988 0.0
1.0 PAYMENT 7541.47 C1175418534 14060.77 6519.3 M1442038800 0.0 0.0 -7541.47 0.0
1.0 DEBIT 3635.93 C296541232 106929.0 103293.07 C288665596 95816.79 8496.61 -3635.929999999993 -87320.18
1.0 PAYMENT 1328.59 C1997357673 14469.0 13140.41 M1250603112 0.0 0.0 -1328.5900000000001 0.0
1.0 TRANSFER 56145.85 C26357357 21528.0 0.0 C1058634310 5075.0 33008.44 -21528.0 27933.440000000002
1.0 PAYMENT 973.18 C1990733619 10762.0 9788.82 M1806378373 0.0 0.0 -973.1800000000003 0.0
1.0 PAYMENT 619.24 C1958592872 31960.0 31340.76 M1682733268 0.0 0.0 -619.2400000000016 0.0
1.0 DEBIT 300.41 C1395262169 39167.0 38866.59 C736709391 48749.55 46820.71 -300.4100000000035 -1928.8400000000038
1.0 TRANSFER 700967.19 C1629454900 15383.0 0.0 C1060830840 50767.43 130747.56 -15383.0 79980.13
1.0 PAYMENT 8354.27 C2075372030 5611.0 0.0 M2092885124 0.0 0.0 -5611.0 0.0
1.0 DEBIT 5423.28 C2139168000 20281.0 14857.72 C367746789 11523.0 0.0 -5423.280000000001 -11523.0
1.0 PAYMENT 708.59 C1653751526 14857.72 14149.13 M67671827 0.0 0.0 -708.5900000000001 0.0
1.0 PAYMENT 8358.81 C253348306 3467.0 0.0 M2013768748 0.0 0.0 -3467.0 0.0
1.0 PAYMENT 2245.62 C194107588 30910.0 28664.38 M1264674474 0.0 0.0 -2245.619999999999 0.0
1.0 PAYMENT 4593.59 C175595853 78282.0 73688.41 M1339345635 0.0 0.0 -4593.5899999999965 0.0
1.0 PAYMENT 399.66 C1241938981 153760.0 153360.34 M168957945 0.0 0.0 -399.6600000000035 0.0
1.0 PAYMENT 1617.9 C1004430079 507865.0 506247.1 M1379148981 0.0 0.0 -1617.9000000000233 0.0
1.0 PAYMENT 2167.26 C732597634 13669.0 11501.74 M832432849 0.0 0.0 -2167.26 0.0
1.0 PAYMENT 9738.95 C2031927175 289748.0 280009.05 M176041373 0.0 0.0 -9738.950000000012 0.0
1.0 PAYMENT 3396.25 C1967617997 18524.0 15127.75 M726843606 0.0 0.0 -3396.25 0.0
1.0 PAYMENT 6780.78 C925803196 52640.0 45859.22 M1288135425 0.0 0.0 -6780.779999999999 0.0
1.0 PAYMENT 2284.54 C49318987 539.0 0.0 M1058650291 0.0 0.0 -539.0 0.0
-- Organize by Type
select type, count(1) from financials group by type
type count(1)
TRANSFER 532909.0
CASH_IN 1399284.0
CASH_OUT 2237500.0
PAYMENT 2151495.0
DEBIT 41432.0
select type, sum(amount) from financials group by type
type sum(amount)
TRANSFER 4.852919872631695e11
CASH_IN 2.3636739191246045e11
CASH_OUT 3.9441299522449023e11
PAYMENT 2.809337113837001e10
DEBIT 2.2719922127999982e8

Rules-based Model: Create a set of rules to identify fraud based on known cases

The following where clause are a set of rules to identify know fraud-based cases using SQL; i.e. rules-based model. * Often, financial fraud analytics start with with clauses like the where clause below * Note, in reality, rules are often much larger and more complicated

from pyspark.sql import functions as F

# Rules to Identify Known Fraud-based
df = df.withColumn("label", 
                   F.when(
                     (
                       (df.oldbalanceOrg <= 56900) & (df.type == "TRANSFER") & (df.newbalanceDest <= 105)) | 
                       (
                         (df.oldbalanceOrg > 56900) & (df.newbalanceOrig <= 12)) | 
                           (
                             (df.oldbalanceOrg > 56900) & (df.newbalanceOrig > 12) & (df.amount > 1160000)
                           ), 1
                   ).otherwise(0))

# Calculate proportions
fraud_cases = df.filter(df.label == 1).count()
total_cases = df.count()
fraud_pct = 1.*fraud_cases/total_cases

# Provide quick statistics
print("Based on these rules, we have flagged %s (%s) fraud cases out of a total of %s cases." % (fraud_cases, fraud_pct, total_cases))

# Create temporary view to review data
df.createOrReplaceTempView("financials_labeled")
Based on these rules, we have flagged 255640 (0.04017841706718302) fraud cases out of a total of 6362620 cases.

How much fraud are we talking about?

Based on the existing rules, while 4% of the transactions are fraudulent, it takes into account of the 11% of the total amount.

select label, count(1) as `Transactions`, sum(amount) as `Total Amount` from financials_labeled group by label
label Transactions Total Amount
1.0 255640.0 1.2932429667647998e11
0.0 6106980.0 1.0150686480832826e12

Top Origination / Destination Difference Pairs (>$1M TotalDestDiff)

Each bar represents a pair of entities performing a transaction

-- where sum(destDiff) >= 10000000.00
select nameOrig, nameDest, label, TotalOrgDiff, TotalDestDiff
  from (
     select nameOrig, nameDest, label, sum(OrgDiff) as TotalOrgDiff, sum(destDiff) as TotalDestDiff 
       from financials_labeled 
      group by nameOrig, nameDest, label 
     ) a
 where TotalDestDiff >= 1000000
 limit 100
nameOrig nameDest label TotalOrgDiff TotalDestDiff
C752686443 C1816757085 0.0 -51178.0 1037297.459999999
C1034899044 C306206744 0.0 0.0 2308448.1800000006
C1337803451 C1778801068 0.0 0.0 3155647.91
C1570013189 C1945802665 1.0 -63916.0 1179067.2799999998
C1379731404 C413177525 0.0 0.0 2375262.37
C608080514 C2006081398 1.0 -101396.29 3208776.46
C1567963311 C1141049797 0.0 136177.75999999978 1243657.54
C582291919 C461640598 0.0 0.0 3381134.63
C689413511 C1988852187 0.0 0.0 1161908.05
C122861681 C1754722089 0.0 -22291.0 1512938.6399999997
C1088745859 C2049813033 0.0 -42044.0 1806348.1500000001
C1006778810 C1825389809 0.0 0.0 2681653.25
C1440763981 C1436393926 0.0 35711.119999999995 1117034.7
C50202201 C663929766 0.0 -10293.0 3159176.9200000004
C1532100961 C1049661557 0.0 -32755.0 1668053.03
C1538752236 C791710001 0.0 -76350.53000000003 1780469.75
C700323381 C988406186 0.0 60388.73999999999 8117469.14
C2132142410 C1558550579 0.0 0.0 4227345.199999999
C2060552204 C683891649 0.0 -253.0 5932193.2
C1809115549 C2115178036 0.0 -34061.37 5844315.460000001
C1159220227 C529351377 0.0 22624.52000000002 1274909.27
C756046909 C1151964959 0.0 -37504.0 3953550.38
C77003778 C530216580 0.0 -16420.0 2711634.1
C945058760 C5958610 1.0 -57367.0 2136408.2
C1627314039 C1558550579 0.0 0.0 3263061.3899999997
C1768965319 C1553217607 0.0 194027.76999999955 1382299.6
C1455537694 C1917025677 0.0 -43924.17000000016 1371223.48
C748697380 C1080314178 0.0 99377.57 1.13349226e7
C1951089624 C489693281 0.0 -22967.26 2387625.5200000005
C1964002964 C744708435 0.0 0.0 1489751.1300000001
C1203268505 C459903071 0.0 0.0 1769126.75
C1718688489 C1469682037 0.0 -42999.0 1013108.0999999999
C1645613622 C2047015557 0.0 133130.81000000006 1009068.89
C1385602675 C1790831319 1.0 -274602.45 2445953.84
C1674767172 C1988058126 0.0 81933.46999999974 1731338.83
C1888296222 C276384108 0.0 0.0 3108280.4000000004
C1282343859 C1360767589 1.0 -104066.0 2266055.2300000004
C1560116595 C1876351111 0.0 0.0 3266580.3800000004
C423919427 C1018394275 0.0 0.0 2625447.23
C819894239 C1247600089 0.0 0.0 5414463.799999999
C1773614696 C232933235 0.0 -27745.01000000001 1345923.1099999999
C1422230471 C769591652 0.0 89261.90000000002 2713617.45
C1782925064 C1839337592 0.0 0.0 1757275.6799999997
C99199522 C934300202 0.0 -10200.0 1167812.36
C180184286 C700689755 1.0 -74712.0 3596897.9699999997
C1638181666 C749013731 0.0 0.0 2214655.4299999997
C767211382 C1393612077 0.0 0.0 2286610.52
C234678587 C797657283 1.0 -124929.0 1154001.7000000002
C20269622 C2107960573 1.0 -237119.41 1130794.31
C598082082 C1955746028 0.0 0.0 2546000.1399999997
C325277081 C1730964371 0.0 0.0 1711671.65
C810441806 C218165429 0.0 -31031.04 1744178.31
C1383044705 C767544292 1.0 -130542.0 1030078.6299999999
C956369822 C453306510 0.0 -2627.11 2845873.34
C93288387 C1485516154 0.0 0.0 1818114.9499999993
C1701105060 C987174960 1.0 -1019339.51 1537040.6799999997
C749653721 C1778404446 0.0 0.0 3217781.69
C137669742 C805814824 0.0 0.0 4403477.52
C1990342884 C1127443868 0.0 0.0 1068271.4699999997
C1433651588 C1379031360 0.0 0.0 1260404.5499999989
C124283794 C239330900 0.0 -1.0 1368747.4699999997
C330853726 C2141828226 0.0 -37689.0 2658344.96
C2053130194 C1685697739 0.0 0.0 1079265.73
C2065848959 C261292810 0.0 0.0 1096327.63
C1187190307 C915733493 0.0 -9028.0 2118780.33
C42014603 C2078745453 0.0 57818.42 1954231.5700000003
C1035196945 C968408272 0.0 0.0 1665205.8000000003
C1584984374 C271588719 0.0 0.0 2111582.98
C61333846 C2145867337 0.0 0.0 1491380.9300000002
C2143321563 C1521704415 0.0 -963.0 1328668.7400000002
C1944196283 C16729522 0.0 -29746.0 3005476.4
C373966597 C1492359254 0.0 -1744.0 1062979.0
C1452543394 C119950815 0.0 0.0 1482459.4100000001
C1232347955 C651049041 0.0 424585.81 1113506.17
C1838683678 C815306112 0.0 -1347.0 2815123.61
C1960113192 C1437781451 0.0 151030.04999999888 1393690.8000000003
C418409319 C516516576 0.0 0.0 1234482.98
C1443215275 C541484789 0.0 0.0 2095243.3099999998
C1289543366 C578294406 1.0 -60850.0 1808419.65
C1540598314 C1950696591 0.0 0.0 1765025.5999999996
C778684365 C254669696 0.0 -728.0 3582937.19
C1538133757 C1433627902 0.0 -8125.0 1291187.17
C897984995 C904765844 1.0 -293009.18 2224366.4
C1855151198 C805643982 0.0 35591.80000000028 1932983.4300000002
C401005628 C776749939 0.0 0.0 2175311.59
C1122629781 C1991184172 0.0 -5366.0 1167464.57
C974166894 C1363208344 0.0 0.0 1476888.1300000001
C19787815 C1858098329 0.0 0.0 1015287.6800000002
C1902875287 C105223833 0.0 -223.0 3422145.88
C1882353435 C1481851679 0.0 327635.45 2305918.1499999994
C1035277970 C317983984 0.0 -2658.0 1012950.8
C698935038 C1852790850 0.0 0.0 1177790.5
C2081633996 C214555146 0.0 -5626.0 2124608.71
C1763900287 C24176920 0.0 -42522.0 2674988.2799999993
C1729080591 C1045308594 0.0 0.0 1643239.79
C235819446 C311573688 1.0 -1175734.28 2214979.5399999996
C752293219 C1523549102 1.0 -221344.0 1974939.9
C2117929123 C931773845 0.0 0.0 1195544.19
C788474781 C210970463 0.0 -21308.0 1034059.37
C505005314 C1298763451 0.0 -536.0 1203170.67

What type of transactions are associated with fraud?

Reviewing the rules-based model, it appears that most fraudulent transactions are in the category of Transfer and Cash_Out.

select type, label, count(1) as `Transactions` from financials_labeled group by type, label
type label Transactions
PAYMENT 0.0 2150909.0
CASH_OUT 0.0 2052790.0
DEBIT 1.0 31.0
TRANSFER 1.0 70248.0
CASH_OUT 1.0 184710.0
PAYMENT 1.0 586.0
DEBIT 0.0 41401.0
CASH_IN 0.0 1399219.0
TRANSFER 0.0 462661.0
CASH_IN 1.0 65.0

Rules vs. ML model

Instead of creating specific rules that will change over time, can we be more precise and go to production faster by creating a ML model?

Decision Trees

Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.

Because of these facets, decision trees often perform well on top of rules-based models and are often a good starting point for fraud detection.

Source: The Wise Old Tree

Create training and test datasets

To build and validate our generalized fraud ML model, we will initially split the data using randomSplit to create our training and test datasets.

# Initially split our dataset between training and test datasets
(train, test) = df.randomSplit([0.8, 0.2], seed=12345)

# Cache the training and test datasets
train.cache()
test.cache()

# Print out dataset counts
print("Total rows: %s, Training rows: %s, Test rows: %s" % (df.count(), train.count(), test.count()))
Total rows: 6362620, Training rows: 5090394, Test rows: 1272226

Create ML Pipeline

When creating an ML model, there are typically a set of repeated steps (e.g. StringIndexer, VectorAssembler, etc.). By creating a ML pipeline, we can reuse this pipeline (and all of its steps) to retrain on a new and/or updated dataset.

from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import OneHotEncoderEstimator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier

# Encodes a string column of labels to a column of label indices
indexer = StringIndexer(inputCol = "type", outputCol = "typeIndexed")

# VectorAssembler is a transformer that combines a given list of columns into a single vector column
va = VectorAssembler(inputCols = ["typeIndexed", "amount", "oldbalanceOrg", "newbalanceOrig", "oldbalanceDest", "newbalanceDest", "orgDiff", "destDiff"], outputCol = "features")

# Using the DecisionTree classifier model
dt = DecisionTreeClassifier(labelCol = "label", featuresCol = "features", seed = 54321, maxDepth = 5)

# Create our pipeline stages
pipeline = Pipeline(stages=[indexer, va, dt])
# View the Decision Tree model (prior to CrossValidator)
dt_model = pipeline.fit(train)
display(dt_model.stages[-1])
treeNode
{"index":13,"featureType":"continuous","prediction":null,"threshold":-59767.5,"categories":null,"feature":6,"overflow":false}
{"index":1,"featureType":"continuous","prediction":null,"threshold":17.18,"categories":null,"feature":3,"overflow":false}
{"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":7,"featureType":"continuous","prediction":null,"threshold":8142508.215,"categories":null,"feature":2,"overflow":false}
{"index":3,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false}
{"index":2,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":5,"featureType":"continuous","prediction":null,"threshold":2296867.575,"categories":null,"feature":2,"overflow":false}
{"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":9,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false}
{"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":11,"featureType":"continuous","prediction":null,"threshold":744825.315,"categories":null,"feature":4,"overflow":false}
{"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":21,"featureType":"continuous","prediction":null,"threshold":-38090.5,"categories":null,"feature":6,"overflow":false}
{"index":19,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false}
{"index":17,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false}
{"index":15,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0],"feature":0,"overflow":false}
{"index":14,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":16,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":20,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":23,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0,2.0,4.0],"feature":0,"overflow":false}
{"index":22,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":27,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false}
{"index":25,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false}
{"index":24,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":26,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":28,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}

Use BinaryClassificationEvaluator

Determine the accuracy of the model by reviewing the areaUnderPR and areaUnderROC

from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Use BinaryClassificationEvaluator to evaluate our model
evaluatorPR = BinaryClassificationEvaluator(labelCol = "label", rawPredictionCol = "prediction", metricName = "areaUnderPR")
evaluatorAUC = BinaryClassificationEvaluator(labelCol = "label", rawPredictionCol = "prediction", metricName = "areaUnderROC")

Setup CrossValidation

To try out different parameters to potentially improve our model, we will use CrossValidator in conjunction with the ParamGridBuilder to automate trying out different parameters.

Note, we are using evaluatorPR as our evaluator as the Precision-Recall curve is often better for an unbalanced distribution.

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

# Build the grid of different parameters
paramGrid = ParamGridBuilder() \
    .addGrid(dt.maxDepth, [5, 10, 15]) \
    .addGrid(dt.maxBins, [10, 20, 30]) \
    .build()

# Build out the cross validation
crossval = CrossValidator(estimator = dt,
                          estimatorParamMaps = paramGrid,
                          evaluator = evaluatorPR,
                          numFolds = 3)  

pipelineCV = Pipeline(stages=[indexer, va, crossval])

# Train the model using the pipeline, parameter grid, and preceding BinaryClassificationEvaluator
cvModel_u = pipelineCV.fit(train)

Review Results

Review the areaUnderPR (area under Precision Recall curve) and areaUnderROC (area under Receiver operating characteristic) or AUC (area under curve) metrics.

# Build the best model (training and test datasets)
train_pred = cvModel_u.transform(train)
test_pred = cvModel_u.transform(test)

# Evaluate the model on training datasets
pr_train = evaluatorPR.evaluate(train_pred)
auc_train = evaluatorAUC.evaluate(train_pred)

# Evaluate the model on test datasets
pr_test = evaluatorPR.evaluate(test_pred)
auc_test = evaluatorAUC.evaluate(test_pred)

# Print out the PR and AUC values
print("PR train:", pr_train)
print("AUC train:", auc_train)
print("PR test:", pr_test)
print("AUC test:", auc_test)
PR train: 0.9537894984523128
AUC train: 0.998647996459481
PR test: 0.9539170535377599
AUC test: 0.9984378183482442

Confusion Matrix Code-base

Subsequent cells will be using the following code to plot the confusion matrix.

# Create confusion matrix template
from pyspark.sql.functions import lit, expr, col, column

# Confusion matrix template
cmt = spark.createDataFrame([(1, 0), (0, 0), (1, 1), (0, 1)], ["label", "prediction"])
cmt.createOrReplaceTempView("cmt")
# Source code for plotting confusion matrix is based on `plot_confusion_matrix` 
# via https://runawayhorse001.github.io/LearningApacheSpark/classification.html#decision-tree-classification
import matplotlib.pyplot as plt
import numpy as np
import itertools

def plot_confusion_matrix(cm, title):
  # Clear Plot
  plt.gcf().clear()

  # Configure figure
  fig = plt.figure(1)
  
  # Configure plot
  classes = ['Fraud', 'No Fraud']
  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
  plt.title(title)
  plt.colorbar()
  tick_marks = np.arange(len(classes))
  plt.xticks(tick_marks, classes, rotation=45)
  plt.yticks(tick_marks, classes)

  # Normalize and establish threshold
  normalize=False
  fmt = 'd'
  thresh = cm.max() / 2.

  # Iterate through the confusion matrix cells
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
      plt.text(j, i, format(cm[i, j], fmt),
               horizontalalignment="center",
               color="white" if cm[i, j] > thresh else "black")

  # Final plot configurations
  plt.tight_layout()
  plt.ylabel('True label')
  plt.xlabel('Predicted label') 
  
  # Display images
  image = fig
  
  # Show plot
  #fig = plt.show()
  
  # Save plot
  fig.savefig("confusion-matrix.png")

  # Display Plot
  display(image)
  
  # Close Plot
  plt.close(fig)
# Create temporary view for test predictions
test_pred.createOrReplaceTempView("test_pred")

# Create test predictions confusion matrix
test_pred_cmdf = spark.sql("select a.label, a.prediction, coalesce(b.count, 0) as count from cmt a left outer join (select label, prediction, count(1) as count from test_pred group by label, prediction) b on b.label = a.label and b.prediction = a.prediction order by a.label desc, a.prediction desc")

# View confusion matrix
display(test_pred_cmdf)
label prediction count
1.0 1.0 50717.0
1.0 0.0 58.0
0.0 1.0 2421.0
0.0 0.0 1219030.0

View Confusion Matrix

Let's use matplotlib and pandas to visualize our confusion matrix

# Convert to pandas
cm_pdf = test_pred_cmdf.toPandas()

# Create 1d numpy array of confusion matrix values
cm_1d = cm_pdf.iloc[:, 2]

# Create 2d numpy array of confusion matrix values
cm = np.reshape(cm_1d, (-1, 2))

# Print out the 2d array
print(cm)
[[  50717      58]
 [   2421 1219030]]
# Plot confusion matrix  
plot_confusion_matrix(cm, "Confusion Matrix (Unbalanced Test)")
# Log MLflow
with mlflow.start_run(experiment_id = mlflow_experiment_id) as run:
  # Log Parameters and metrics
  mlflow.log_param("balanced", "no")
  mlflow.log_metric("PR train", pr_train)
  mlflow.log_metric("AUC train", auc_train)
  mlflow.log_metric("PR test", pr_test)
  mlflow.log_metric("AUC test", auc_test)
  
  # Log model
  mlflow.spark.log_model(dt_model, "model")
  
  # Log Confusion matrix
  mlflow.log_artifact("confusion-matrix.png")

Model with Balanced classes

Let's see if we can improve our decision tree model but balancing the Fraud vs. No Fraud cases. We will tune the model using the metrics areaUnderROC or (AUC)

# Reset the DataFrames for no fraud (`dfn`) and fraud (`dfy`)
dfn = train.filter(train.label == 0)
dfy = train.filter(train.label == 1)

# Calculate summary metrics
N = train.count()
y = dfy.count()
p = y/N

# Create a more balanced training dataset
train_b = dfn.sample(False, p, seed = 92285).union(dfy)

# Print out metrics
print("Total count: %s, Fraud cases count: %s, Proportion of fraud cases: %s" % (N, y, p))
print("Balanced training dataset count: %s" % train_b.count())
Total count: 5090394, Fraud cases count: 204865, Proportion of fraud cases: 0.040245411258932016
Balanced training dataset count: 401898
# Display our more balanced training dataset
display(train_b.groupBy("label").count())
label count
1.0 204865.0
0.0 197033.0

Update ML pipeline

Because we had created the ML pipeline stages in the previous cells, we can re-use them to execute it against our balanced dataset.

Note, we are using evaluatorAUC as our evaluator as this is often better for a balanced distribution.

# Re-run the same ML pipeline (including parameters grid)
crossval_b = CrossValidator(estimator = dt,
                          estimatorParamMaps = paramGrid,
                          evaluator = evaluatorAUC,
                          numFolds = 3)  

pipelineCV_b = Pipeline(stages=[indexer, va, crossval_b])

# Train the model using the pipeline, parameter grid, and BinaryClassificationEvaluator using the `train_b` dataset
cvModel_b = pipelineCV_b.fit(train_b)
# Build the best model (balanced training and full test datasets)
train_pred_b = cvModel_b.transform(train_b)
test_pred_b = cvModel_b.transform(test)

# Evaluate the model on the balanced training datasets
pr_train_b = evaluatorPR.evaluate(train_pred_b)
auc_train_b = evaluatorAUC.evaluate(train_pred_b)

# Evaluate the model on full test datasets
pr_test_b = evaluatorPR.evaluate(test_pred_b)
auc_test_b = evaluatorAUC.evaluate(test_pred_b)

# Print out the PR and AUC values
print("PR train:", pr_train_b)
print("AUC train:", auc_train_b)
print("PR test:", pr_test_b)
print("AUC test:", auc_test_b)
PR train: 0.999629161563572
AUC train: 0.9998071389056655
PR test: 0.9904709171789063
AUC test: 0.9997903902204509
# Create temporary view for test predictions
test_pred_b.createOrReplaceTempView("test_pred_b")

# Create test predictions confusion matrix
test_pred_b_cmdf = spark.sql("select a.label, a.prediction, coalesce(b.count, 0) as count from cmt a left outer join (select label, prediction, count(1) as count from test_pred_b group by label, prediction) b on b.label = a.label and b.prediction = a.prediction order by a.label desc, a.prediction desc")

# View confusion matrix
display(test_pred_b_cmdf)
label prediction count
1.0 1.0 50774.0
1.0 0.0 1.0
0.0 1.0 488.0
0.0 0.0 1220963.0
# Convert to pandas
cm_b_pdf = test_pred_b_cmdf.toPandas()

# Create 1d numpy array of confusion matrix values
cm_b_1d = cm_b_pdf.iloc[:, 2]

# Create 2d numpy array of confusion matrix vlaues
cm_b = np.reshape(cm_b_1d, (-1, 2))

# Print out the 2d array
print(cm_b)
[[  50774       1]
 [    488 1220963]]

View Decision Tree Models

Visually compare the differences between the unbalanced and balanced decision tree models (basd on the train and train_b datasets respectively).

# Extract Feature Importance
#  Attribution: Feature Selection Using Feature Importance Score - Creating a PySpark Estimator
#               https://www.timlrx.com/2018/06/19/feature-selection-using-feature-importance-score-creating-a-pyspark-estimator/
import pandas as pd

def ExtractFeatureImp(featureImp, dataset, featuresCol):
    list_extract = []
    for i in dataset.schema[featuresCol].metadata["ml_attr"]["attrs"]:
        list_extract = list_extract + dataset.schema[featuresCol].metadata["ml_attr"]["attrs"][i]
    varlist = pd.DataFrame(list_extract)
    varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])
    return(varlist.sort_values('score', ascending = False))
# View the Unbalanced Decision Tree model (prior to CrossValidator)
dt_model = pipeline.fit(train)
display(dt_model.stages[-1])
treeNode
{"index":13,"featureType":"continuous","prediction":null,"threshold":-59767.5,"categories":null,"feature":6,"overflow":false}
{"index":1,"featureType":"continuous","prediction":null,"threshold":17.18,"categories":null,"feature":3,"overflow":false}
{"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":7,"featureType":"continuous","prediction":null,"threshold":8142508.215,"categories":null,"feature":2,"overflow":false}
{"index":3,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false}
{"index":2,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":5,"featureType":"continuous","prediction":null,"threshold":2296867.575,"categories":null,"feature":2,"overflow":false}
{"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":9,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false}
{"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":11,"featureType":"continuous","prediction":null,"threshold":744825.315,"categories":null,"feature":4,"overflow":false}
{"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":21,"featureType":"continuous","prediction":null,"threshold":-38090.5,"categories":null,"feature":6,"overflow":false}
{"index":19,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false}
{"index":17,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false}
{"index":15,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0],"feature":0,"overflow":false}
{"index":14,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":16,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":20,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":23,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0,2.0,4.0],"feature":0,"overflow":false}
{"index":22,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":27,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false}
{"index":25,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false}
{"index":24,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":26,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":28,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
# Extract Feature Importance for the original unbalanced dt_model
ExtractFeatureImp(dt_model.stages[-1].featureImportances, train_pred, "features").head(10)
<span class="ansired">Out[</span><span class="ansired">47</span><span class="ansired">]: </span>
   idx            name                                           vals  \
6    6         orgDiff                                            NaN   
3    3  newbalanceOrig                                            NaN   
5    5  newbalanceDest                                            NaN   
2    2   oldbalanceOrg                                            NaN   
0    0     typeIndexed  [CASH_OUT, PAYMENT, CASH_IN, TRANSFER, DEBIT]   
1    1          amount                                            NaN   
4    4  oldbalanceDest                                            NaN   
7    7        destDiff                                            NaN   

      score  
6  0.609449  
3  0.382837  
5  0.005051  
2  0.001903  
0  0.000621  
1  0.000128  
4  0.000010  
7  0.000000  
# View the Balanced Decision Tree model (prior to CrossValidator)
dt_model_b = pipeline.fit(train_b)
display(dt_model_b.stages[-1])
treeNode
{"index":15,"featureType":"continuous","prediction":null,"threshold":-39863.59500000001,"categories":null,"feature":6,"overflow":false}
{"index":7,"featureType":"continuous","prediction":null,"threshold":6.26,"categories":null,"feature":3,"overflow":false}
{"index":5,"featureType":"continuous","prediction":null,"threshold":51488.5,"categories":null,"feature":2,"overflow":false}
{"index":3,"featureType":"continuous","prediction":null,"threshold":201.245,"categories":null,"feature":5,"overflow":false}
{"index":1,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,2.0],"feature":0,"overflow":false}
{"index":0,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":2,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":13,"featureType":"continuous","prediction":null,"threshold":914660.585,"categories":null,"feature":1,"overflow":false}
{"index":11,"featureType":"continuous","prediction":null,"threshold":50.19,"categories":null,"feature":3,"overflow":false}
{"index":9,"featureType":"continuous","prediction":null,"threshold":51488.5,"categories":null,"feature":2,"overflow":false}
{"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":14,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":21,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,2.0,3.0,4.0],"feature":0,"overflow":false}
{"index":17,"featureType":"continuous","prediction":null,"threshold":914660.585,"categories":null,"feature":1,"overflow":false}
{"index":16,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":19,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0],"feature":0,"overflow":false}
{"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":20,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":23,"featureType":"continuous","prediction":null,"threshold":201.245,"categories":null,"feature":5,"overflow":false}
{"index":22,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
{"index":24,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false}
# Extract Feature Importance for the nbalanced dt_model
ExtractFeatureImp(dt_model_b.stages[-1].featureImportances, train_pred_b, "features").head(10)
<span class="ansired">Out[</span><span class="ansired">49</span><span class="ansired">]: </span>
   idx            name                                           vals  \
6    6         orgDiff                                            NaN   
3    3  newbalanceOrig                                            NaN   
2    2   oldbalanceOrg                                            NaN   
5    5  newbalanceDest                                            NaN   
1    1          amount                                            NaN   
0    0     typeIndexed  [CASH_OUT, TRANSFER, PAYMENT, CASH_IN, DEBIT]   
4    4  oldbalanceDest                                            NaN   
7    7        destDiff                                            NaN   

      score  
6  0.901013  
3  0.057844  
2  0.027848  
5  0.010055  
1  0.002049  
0  0.001190  
4  0.000000  
7  0.000000  

Comparing Confusion Matrices

Below we will compare the unbalanced and balanced decision tree ML model confusion matrices.

# Plot confusion matrix  
plot_confusion_matrix(cm, "Confusion Matrix (Unbalanced Test)")
# Plot confusion matrix  
plot_confusion_matrix(cm_b, "Confusion Matrix (Balanced Test)")
# Log MLflow
with mlflow.start_run(experiment_id = mlflow_experiment_id) as run:
  # Log Parameters and metrics
  mlflow.log_param("balanced", "yes")
  mlflow.log_metric("PR train", pr_train_b)
  mlflow.log_metric("AUC train", auc_train_b)
  mlflow.log_metric("PR test", pr_test_b)
  mlflow.log_metric("AUC test", auc_test_b)
    
  # Log model
  mlflow.spark.log_model(dt_model_b, "model")
  
  # Log Confusion matrix
  mlflow.log_artifact("confusion-matrix.png")

Observation

There is a significant difference for the PR and AUC values when comparing the unbalanced vs. balanced decision tree models.

Metric/datasetUnbalancedBalanced
PR train0.95378949845231280.999629161563572
AUC train0.9986479964594810.9998071389056655
PR test0.95391705353775990.9904709171789063
AUC test0.99843781834824420.9997903902204509

You can also view this data directly within MLflow by comparing the unbalanced vs. balanced decision tree models as noted in the following animated GIF.